State re-sharding can reduce cross-shard transaction ratios, which improves the scalability of blockchain systems. However, unavoidable cross-shard transactions and account-locking mechanisms can lead to security risks (read-write conflicts) and performance bottlenecks (low synchronization efficiency). Therefore, this paper proposes a secure and efficient read-write synchronization model for cross-shard transactions in blockchain state re-sharding via a lightweight Global State Tree ($\mathcal{GT}$). The model consists of intra-shard and inter-shard state consistency modules. The intra-shard module includes two methods: account state read-write and account record update. The former allows local shard committees to track account state changes and prevent the use of expired account states, while the latter incorporates account records within maximum latency into an account state data structure, thereby enhancing the traceability and verification efficiency of update history. In the inter-shard module, a transaction processing method with a global takeover mechanism is proposed during the re-sharding window. By using validated data in the$\mathcal{GT}$, the method achieves non-blocking global coordination and account reallocation. Experimental results demonstrate that the proposed model increases transaction throughput and reduces transaction latency compared to bases under Byzantine conditions.

In the field of management research, complex decision-making scenarios are frequently encountered. Intelligent decision-making games serve as an essential tool for simulating such scenarios, enabling decision-makers to evaluate strategies and allocate resources more effectively. However, traditional intelligent decision-making games relying on deep reinforcement learning (DRL) often suffer from prolonged training times, convergence difficulties, and challenges in multiagent coordination. To address these limitations, this study proposes an enhanced game framework that integrates role-based collaboration (RBC) with the environment, class, agent, role, group, object (E-CARGO model). In this framework, agents are first assigned different roles, after which reinforcement learning (RL) techniques are applied for policy training. Simulation experiments conducted on the winning-first platform demonstrate that the proposed method achieves superior convergence performance and agent intelligence compared with conventional RL approaches, effectively mitigating the identified challenges and enhancing overall decision-making efficiency.

It is challenging to achieve an optimal worker allocation for production lines of large-scale industrial enterprises due to the complex requirements for workers’ capabilities. Some optimization approaches have been proposed to solve this problem from different perspectives. However, they have not fully explored the multidimensional features of both workers and production lines, making it hard to obtain an optimal allocation. This article proposes a novel approach to collaborative allocation optimization of production line workers by incorporating multidimensional feature measurement and the environment-classes, agents, roles, and objects (E-CARGO) model. First, we develop a comprehensive evaluation system to quantify the diverse features of workers and production lines. Based on it, an adaptability assessment mechanism is designed to measure the matching degree of workers for different production lines. Afterward, the role-based collaboration theory and the E-CARGO model are innovatively utilized to formalize the worker allocation problem. Meanwhile, the key constraints are identified to guarantee the reasonability of allocation, and an efficient solution via CPLEX package is proposed. Finally, the case analysis and simulation experiments verify the effectiveness of the proposed approach.

Timely and effective early warning is essential for proactive intervention to mitigate students’ learning risks. Existing studies on learning early warning primarily predict students’ knowledge mastery based on academic performance. However, they lack the assessment of course development objective achievement. According to the outcome-based education (OBE) concept, the course objective achievement serves as the foundation for comprehensive student assessment spanning knowledge acquisition, learning ability, and learning attitude. Using the course objective achievement as a guide for learning early warning can help to obtain more objective and accurate warning results. This article proposes a novel approach to learning early warning guided by course objective achievement via knowledge state and learning state modeling. This approach constructs knowledge states related to course objectives through a deep knowledge tracing model and derives the learning states comprising learning ability and learning attitude from multidimensional learning behavior data. The achievement state, fusing the knowledge and learning states, is then fed into a transformer model to capture the temporal dynamics of the achievement state and predict the achievement levels for each course objective. Based on these predictions, a four-level warning rule is employed to assess students’ learning risks. Experiments based on two real-world datasets demonstrate the effectiveness and superiority of the proposed approach. The approach provides a new research paradigm and a feasible solution to achieve accurate personalized early learning warning.

In the indivisible public goods allocation problem (IPGAP), it is always impossible to obtain a perfect allocation plan which can satisfy every group. To eliminate collective bias, decision-makers often lean toward applying the fifty-fifty compromise principle, which means compromising absolutely fairly among all groups. For this concern, a few relevant research investigates the effectiveness of utilizing this principle from a computational perspective due to the lack of quantitative analysis tools. Notably, the environment-classes, agents, roles, groups, and objects (E-CARGO) model, a mature and proven effective tool, demonstrates outstanding performance in the study of such social issues. With respect to the E-CARGO model and its submodel group role assignment (GRA), this article formalizes and explores the IPGAP. Based on the group role assignment with constraints (GRA+), this article provides novel insight into the effectiveness of fifty-fifty compromise, which can inspire decision-makers that unthinkingly conducting the fifty-fifty compromise may not always succeed in eliminating collective bias. Relevant large-scale simulation experiments are conducted in this article to explore when decision-makers may eliminate the bias between the two groups. This article reveals a social paradox: compromise sometimes may not eliminate group bias, and instead, both sides may be offended.

Task allocation is a critical aspect of teamwork, especially in complex projects such as auditing. In audit task allocation, collaboration among team members is essential, as the success of the project depends not only on individual expertise but also on efficient teamwork. This process is closely related to the GMRA problem, a complex optimization challenge. This study tackles the challenges of task allocation in phased tasks and multitasking within auditing. We propose a sequential and parallel assignment method based on tree-structured tasks, formalized as the TSTAspproblem. Through both theoretical and experimental analyses, we demonstrate its feasibility and effectiveness. Additionally, we introduce improvements using the Gurobi solver and validate their impact. The results show significant optimization in audit task allocation.

Under the current fluctuating market environment, the production industry is facing increasing complexity in product production decisions. For this concern, this study aims to find an assignment with more stability in the face of random fluctuations. It will explore how to handle random fluctuations through simulation of volatility with respect to the industry chain. This article first formalizes the group role assignment in fluctuation (GRAF) problem and uses the improved environments-classes, agents, roles, groups, and objects (E-CARGO) model for assignment solving. Furthermore, it simulates fluctuations through the Monte Carlo method and uses E-CARGO to solve each fluctuating task allocation problem. After that, it quantifies the allocation using three indicators: local overlap degree, global overlap degree, and profit score, thereby solving the stable assignment problem under fluctuating conditions. Moreover, based on the above three quantitative indicators, this article uses weight to provide decision-makers with multistrategy assignment options in actual production. Finally, different distribution fluctuations show our model's adaptability. This article also conducts large-scale simulation experiments on the selected stable assignments to verify the proposed methods. Through the proposed method, we can help enterprises make better decisions and achieve greater profits in a volatile market environment.

Role-based collaboration (RBC) is a role-centered computational paradigm for solving collaborative problems, where group multirole assignment (GMRA) is an important component. This article focuses on leader–member teams, a common organizational structure in project management, and extends the GMRA framework to address two critical challenges. First, evaluating the qualifications of leaders and members is nontrivial due to their distinct responsibilities. To address this, we propose a capability–requirement matching evaluation (CRME) method that applies differentiated mechanisms to assess leaders and members. Second, existing studies mainly maximize overall performance while neglecting task performance balance, which is vital for synchronized progress. To overcome this limitation, we develop a group multirole assignment with balanced task performance (GMRABP) model that incorporates a penalty-augmented objective to maximize team performance while reducing disparities across tasks. Furthermore, two linearized variants, GMRABP-A and GMRABP-B, are introduced to enhance computational efficiency. Extensive experiments and comparative analyses validate the effectiveness of the proposed methods, offering practical strategies for managing projects where both performance maximization and progress coordination are essential.

Since the proposal of the differential evolution (DE) algorithm, most improvement efforts have concentrated on binomial crossover. However, we find that DE variants using exponential crossover can also outperform those using binomial crossover in optimization performance, provided that an appropriate parameter control scheme is employed. To develop a high-performance DE algorithm with exponential crossover, this article introduces an adaptive parameter control and selection strategy for DE with exponential crossover (APEX-DE). The main contributions of APEX-DE are summarized as follows: First, a novel adaptive parameter control (APC) technique is proposed, incorporating an automatically generated crossover rate $CR$ , a dual-stage scale factor $F$ generation mechanism, and a new adaptive strategy for the scale parameter $\sigma _{F}$ . Second, a novel selection mechanism is introduced to replace the classical DE selection, thereby enhancing the ability to escape from local optima. Third, a redirection strategy is developed to regenerate individuals and adjust their scale factor $F$ , enhancing evolutionary potential. APEX-DE is evaluated on a large test suite comprising 88 benchmark functions, as well as on a challenging uncrewed aerial vehicle (UAV) path-planning task in multithreat environments. Experimental evaluation confirms that APEX-DE delivers better performance than a broad set of state-of-the-art algorithms.

Role-based collaboration (RBC) has become a crucial computational approach for task allocation and team coordination, yet three critical research gaps remain unresolved. First, while existing methods treat role importance uniformly, real-world scenarios require differentiated prioritization of roles, which is a gap addressed through role weight vectors that dynamically adjust task significance. Second, current qualification matrices that directly specify agent capabilities lack mechanisms to handle assessment uncertainties, leading this article to propose a novel determination method using intuitionistic fuzzy numbers for robust capability modeling. Third, the absence of systematic conflict categorization frameworks motivates our three-way conflict analysis (TWCA) method that classifies conflicts through hierarchical comparisons of agent competency. Drawing from these considerations, the article presents the weighted group role assignment (GRA) with conflicting constraints problem, aiming to overcome the identified challenges through environments-classes, agents, roles, groups, and objects (E-CARGO) framework. The proposed approach is tested and validated through a series of experiments and comparative analyses to demonstrate its efficacy.

Quality of Service (QoS) prediction faces challenges from location-dependent variability and sparse user-service interactions. Existing methods often struggle to integrate location information (e.g., using fixed weights for spatial attributes) or learn representative features from sparse matrices. This paper proposes a method for Decoupling Location and Preference via a dual-branch architecture for robust QoS prediction under extreme sparsity, called DLP. It integrates location and preference features to address the challenges of sparsity and contextual variability. Unlike conventional single-stream or simple concatenation methods, DLP features a novel dual-branch architecture that decouples heterogeneous features and specializes in processing them: Location context and user-service preferences. The first branch, a location feature extraction network, processes user and service geographical and network information. It utilizes an attention mechanism to dynamically weight spatial attributes (instead of fixed weights) based on their actual impact on QoS and selects the most salient co-location features to model spatial interactions. The second branch, a preference feature extraction network, constructs high-dimensional feature representations from similarity-based user-service vectors derived from the sparse QoS matrix. It employs a multi-layer feature extraction block that hierarchically aggregates intermediate features to compensate for information loss during transformation, thereby capturing richer user/service preferences. Finally, a feature fusion prediction network integrates the learned location and preference features to generate accurate QoS predictions. Ablation studies and analysis validate that each component contributes significantly to performance gains. Extensive experiments on the WS-DREAM dataset show that DLP outperforms 22 baselines across 2.5%–20% sparsity, excelling in throughput prediction (achieving reductions up to 9.07% in Mean Absolute Error and 28.86% in Root Mean Squared Error at 2.5% sparsity) and validating its superior QoS prediction accuracy.

In collaborative systems such as factory operations and physician rostering, it is crucial to assign agents to multiple roles over time while balancing performance, equity, and individual preferences. Traditional group multirole assignment (GMRA) models prioritize performance optimization but often neglect workload fairness and individual preferences, leading to agent demotivation and suboptimal team outcomes. To address this gap, we propose a time-constrained GMRA (TGMRA) framework that extends the classical GMRA into a 3-D (roles, agents, time) model, explicitly integrating temporal constraints and heterogeneous agent requirements. Based on this framework, we further develop two extended models: TGMRA_E, which ensures the equitable distribution of work periods and task quantities, and TGMRA_EP, which integrates individual preferences via a weighted multiobjective optimization strategy. Extensive simulations across various group sizes confirm the effectiveness and robustness of these models. Compared with the baseline GMRA, TGMRA_E significantly reduces workload disparities, while TGMRA_EP improves preference satisfaction by up to 123% with less than 4% performance loss. Our results provide scalable scheduling strategies that balance team performance and individual needs and offer practical guidance on parameter selection for diverse real-world scenarios.

Team management, especially assignment, is important and challenging in governance, administration, and organizations. Managers try to use their wisdom, experience, and knowledge to make the managed team better. However, without effective tools, managers always make mistakes or make decisions with their bias, making the management imperfect. Many cases happen, i.e., that good intentions lead to bad consequences, and that official corruption cannot be extinguished. This paper explores the question of why people often tolerate imperfect management, drawing on the (Environments, Classes, Agents, Roles, Groups, and Objects) E-CARGO model to provide insights from a Group Role Assignment (GRA) perspective. In many organizational and social systems, individuals display resilience and adaptability to imperfect management. Using E-CARGO, we examine the factor of team performance that contributes to tolerance for management imperfections, mainly on role assignment. We use simulations to present that by simply accumulating the individual performance values to express team performance, imperfect assignment does not damage the optimized result much. This study uses a few management styles to simulate imperfect assignment, like, (WUWEIERZHI, i.e., rule by doing nothing), (ZEYOUSHANGGANG, i.e., select the best), and (SHIKEERZHI, i.e., know when to stop).

Aquaculture, as a vital component of the fisheries industry, is assuming an increasingly significant role in meeting the growing global demand for aquatic products. However, the allocation of aquaculture resources has become increasingly complex. Overproduction of a single species can lead to market oversupply, resulting in sharp price declines and substantial profit losses for fishermen. The Environment - Classes, Agents, Roles, Groups, and Objects (E-CARGO) model has shown strong potential in addressing such socio-economic problems. This study extends the Group Multirole Allocation (GMRA) model to formalize and address the Aquaculture Imbalance Problem between Supply and Demand (AISDP). The objective is to maximize total profit while considering market demand, disaster risk, and the potential for oversupply. Extensive simulation experiments reveal that the maximum total profit does not occur at the threshold, but rather beyond it—meaning that even though the unit price decreases, total profit can still be increased by further increasing the stocking quantity. Furthermore, the results suggest that fishermen should select the regulatory parameter based on real-world market dynamics and their risk tolerance. This enables aquaculture enterprises to adopt optimal, diversified decision-making strategies aligned with resource availability and strategic development goals.

Flexible employment remains a critical topic for crowdsourcing platforms. Decision-makers always focus their attention on maximizing operational efficiency, yet rarely prioritize the work experience of crowdsourcing employees. However, suboptimal working conditions contribute to user attrition, ultimately diminishing platform profitability. While the importance of worker experience in crowdsourcing platforms is recognized, the Flexible Task Allocation Problem (FTAP) has seen relatively little computational investigation due to the absence of powerful quantitative analytical tools. A notable exception is the Environment-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model, a mature computational framework with demonstrated efficacy in resolving similar socio-technical challenges. Consequently, this study builds upon the E-CARGO model and its Group Role Assignment (GRA) sub-model to provide a formalization and systematic analysis of the FTAP. By incorporating adjustments for distance and role-switching, the enhanced GRA model can significantly optimize the work experience of crowdsourcing employees, albeit at a slight cost to overall performance. This improvement helps crowdsourcing platforms retain more users and expand their scale. Relevant simulation experiments are conducted in this study to rigorously evaluate the effectiveness of these optimizations.

Social relations are complex. Traditional social network analysis encounters too complex social networks to identify and solve social problems. Thanks to the Environments – Classes, Agents, Roles, Groups, and Objects (E-CARGO) model and the methodology of Role-Based Collaboration (RBC), it is possible to clarify and build role networks to abstract and simplify the social relationships among people or agents.This article clarifies the role relationships from the viewpoint of E-CARGO/RBC, and presents the engineering challenges, which make the establishment of the world role networks impossible for one development team. Therefore, a crowdsourcing platform is required. To meet this requirement, this paper describes our practice in building a crowdsourcing platform that supports the final establishment of role networks for society. This work is the first effort to establish role networks in the world.The practice described in this paper provides good practice for researchers and practitioners to conduct social network analysis and understand the complexity of social organizations.

Prostate cancer is the most common malignancy among Australian men, with over 20 000 new diagnoses each year. Accurate forecasts of its incidence and mortality inform stakeholder decision-making and help mitigate its public health impact. In this context, we introduce cutting-edge lightweight neural networks into the domain of prostate cancer data forecasting with edge intelligence for the first time. To address the issue of overfitting in coarse-grained and small-scale prostate cancer datasets, we employ structurally streamlined models: the Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN), representing two predominant branches of neural networks. The GRU’s simplified gating mechanism maintains excellent long-term dependencies capturing capability while drastically reducing parameter count, and the TCN combines sparse connections, parameter sharing, and causal dilated convolutions for efficient temporal modeling. To further bolster generalization, we integrate multiple regularization strategies, including the snapshot ensemble method. Comparative experiments on three real-world prostate cancer datasets demonstrate that our improved lightweight, high-performance neural networks achieve over 40% higher accuracy than linear time series forecasting suitable for small-scale datasets.

This article proposes a dynamic self-triggered distributed model predictive control algorithm for coupled nonlinear systems facing external disturbances and constraints on state and input variables. A dynamic self-triggered mechanism that combines the advantages of event-triggered and self-triggered strategies is designed to simultaneously reduce the frequencies of both sampling and solving optimization problems. Particularly, the triggering threshold is adaptively adjusted using a dynamic variable, which can effectively balance control performance and computational resources. Furthermore, through the construction of a two-model optimal control problem and the analysis of input-to-state practical stability for the overall system, a single-mode distributed model predictive control framework is established for each subsystem within the proposed algorithm, which enables a fully distributed implementation. Sufficient conditions for recursive feasibility and robust stability are investigated, and conservatism is reduced by eliminating the requirement for the system state to reach the terminal region in finite time. Finally, the effectiveness of the developed algorithm is validated through two numerical examples with comparisons.

Role-based collaboration (RBC) is a role-centered computational approach designed to solve collaboration problems. Group role assignment is an essential and extensive part of this research. Based on group multirole assignment (GMRA), this article addresses some issues in the current research. First, managers often hope to obtain the highest benefits rather than maximizing the team performance, which is emphasized in the traditional RBC research. This article introduces the use of expected utility theory to assign roles in order to maximize team effectiveness. Second, the existing studies need to provide expressions of agent and role conflicts, which have yet to be reasonably addressed. This article classifies conflicts by employing agent and role capability combined with the three-way conflict analysis theory. Based on these, this article puts forward the utility-based GMRA with conflicting agent and role problems. The validity is verified through several experiments and comparative analysis, which provides more possibilities for future research.

Group role assignment (GRA) maximizes total benefits by assigning agents to appropriate roles, while GRA with a training plan (GRATP) further considers the impact of training. However, existing research on GRA and GRATP does not fully consider the demand for flexible adjustment of human resource assignment, which may lead to increased employment costs and project delays. Moreover, role importance significantly affects training resource assignment, as key roles contribute more to overall performance. Therefore, we propose the GRA with secondment and role-importance-based training plan (GRA-SRIT) model to address these issues. Specifically, this article introduces seconded personnel to temporarily replace the positions of agents undergoing training, ensuring the smooth continuation of the project. Depending on the role requirements, different training durations are assigned based on the specific requirements of their roles. In addition, trainers with different levels of expertise are assigned to agents based on role importance, ensuring that critical roles receive more specialized training, thus maximizing total benefit. Finally, experiments demonstrate the proposed model’s effectiveness in different scenarios.

How to identify an emergent property in a social system is a challenging topic. Social system modeling is an effective way to understand and explain the emergence phenomena. A reversed process of Role-Based Collaboration (RBC) is proposed for emergence studies. The Environments–Classes, Agents, Roles, Groups, and Objects (E-CARGO) model is the fundamental model of RBC. A well extended E-CARGO (EE-CARGO) model is proposed to modeling a social system with emergent properties. The system is abstracted into agent-role level, roles are modeled as a tree structure and role emergence is identified and analyzed at multiple scales. Then we derive that the masses are primary, and the individual agent is secondary to understanding the emergence forming. A case study is accomplished to verify the effectiveness of proposed approach (i.e., the reversed RBC process and EE-CARGO model). The results are revealed that the role emergence fits a cubic curve estimation by time and at a moderate scale, the role emergence happens easily. Role performances at a small scale affect super-role emergence at a large scale statistically, and vice versa. The proposed approach can be applied to investigate emergence in a variety of areas, such as social evolution.

Role-based collaboration (RBC) theory is a promising paradigm for problem-solving in complex systems. Multigroup role assignment (MGRA) specifically tackles the task of assigning roles for multigroup collaboration. However, due to the constraint that an agent can only play a role in one group, the current MGRA models are incapable of handling when required agents outnumber the available supply. Group multirole assignment (GMRA) resolves the problem by permitting an agent to be assigned multiple roles, but it cannot address the assignment involving multiple environments-classes, agents, roles, groups, objects (E-CARGO) groups. Therefore, this article presents a comprehensive overview of the GMRA problem in multiple E-CARGO groups under various conditions, generalized as the multigroup multirole assignment (MGMRA) problem. The MGMRA problem primarily revolves around two key factors: the maximum number of roles that an agent can undertake within an E-CARGO group, and the maximum number of different roles across all E-CARGO groups, which have a significant impact on the sufficiency or necessity conditions of the algorithm as well as its performance. Therefore, a unified model and its special cases are proposed to solve the concrete assignment problems under different conditions. The effectiveness of models is verified through comprehensive experiments.

Role-based collaboration (RBC) is an innovative collaborative approach designed to enhance collaboration. Role negotiation (RN) is a critical step in RBC, during which the role set and the number of agents required for each role, i.e., role requirements, are determined. This process establishes the foundational input for group role assignment (GRA), where roles are assigned to agents to optimize group performance. Uncertainties in RN, such as task volume fluctuations, create dynamic agent requirements. However, existing RBC models typically assume RN to be static, thus failing to adequately address the substantial challenges. Three-way decision (3WD) is a robust decision-making methodology well-suited for managing uncertainty. To address the uncertainties in role requirements, this article introduces truncated discrete distribution to quantify role requirements, and presents a novel group role three-way assignment (GR3A) model. Compared with traditional RBC, our model offers an additional variable partial substitute choice that offers agents little salary during nonengagement periods but can transition to full involvement as required according to the prior agreement. GR3A is a dual-objective nonlinear optimization problem, for which a linearization strategy is proposed to achieve the optimal resolution. Additionally, sufficient and necessary conditions for these assignment problems are put forward to enhance the efficacy of the proposed solutions. To our knowledge, this study innovatively introduces a truncated discrete distribution and 3WD into the RBC framework. Empirical validation through simulations demonstrates the effectiveness and efficacy of the proposed method within the RBC context.

Surrogate-assisted evolutionary algorithms (SAEAs) are extensively used to tackle expensive optimization problems (EOPs). The integration of surrogate-based global and local search is a prevalent hierarchical SAEA framework, which can effectively balance exploration and exploitation capabilities. However, it still faces challenges when tackling high-dimensional EOPs (HEOPs) owing to the curse of dimensionality. In this article, we propose a hierarchical surrogate-assisted differential evolution with core space localization (HSADE-CS) to solve HEOPs. Its contributions are listed as follows: 1) a top-promising sampling strategy is introduced in the global search to mitigate the challenges posed by the uncertainty in the performance of the surrogate model; 2) a core space localization (CSL) method is proposed to identify a high-potential space within the local promising region, enhancing the effectiveness of local search; and 3) a fitness-independent adaptive parameter control method based on the Minkowski distance is developed within the differential evolution (DE) optimizer to improve the performance of surrogate model-driven local search. The performance of HSADE-CS has been validated on numerous benchmark problems from the commonly used expensive optimization benchmark suite, as well as the CEC2014 and CEC2017 benchmark suites, with problem dimensions up to 500. It has also been tested on a real-world problem, i.e., circular antenna array design optimization. Experimental results demonstrate that HSADE-CS is highly competitive compared to the state-of-the-art SAEAs.

The human–machine collaboration task-allocation problem involves three major challenges: role diversity, capability heterogeneity, and task dynamics. Most existing studies treat humans and machines as parallel units through a static “Human + Machine” additive paradigm, which neglects the evolution of capability during collaboration. Some works “deeply couple” relatively low-autonomy machines with humans, thereby limiting the system’s flexibility in resource scheduling and dynamic reconfiguration. This study analyzes the problem from a multiagent system perspective and classifies execution units into three types: human agents, machine agents, and human–machine collaborative agents, and distinguishes between their independent and collaborative capabilities. Next, we propose the dynamic short-board balance synergy assessment method, which integrates the “short-board” concept to quantify collaboration performance and leverages agents that have low independent but high collaborative capabilities. By incorporating multiple constraints, we establish the role-based human–machine collaboration (RBHMC) model, prove its NP-hardness, and design a multi-level solving approach to handle small-scale and medium-to-large-scale data separately. The experimental results indicate that, compared with “Human + Machine” and “Deep Coupling” models, RBHMC outperforms in task completion rate, resource utilization, and system robustness. An industrial case study further validates its applicability and superiority in real-world settings. Finally, RBHMC’s transferability is validated through vertical technology adaptation and horizontal scenario migration, providing a scalable solution for multidomain human–machine collaboration in complex scenarios.

Practical Byzantine Fault Tolerance-based consensus mechanisms in consortium blockchains face challenges in scalability and communication efficiency. While recent approaches like HotStuff and Kauri have attempted to address these issues through star and tree communication structures, they still encounter limitations in security, communication costs, and node workload distribution. This article presents CTT, a novel consensus mechanism with a three-layer tree communication structure for consortium blockchains. CTT incorporates three key innovations: 1) A fixed three-layer architecture that reduces communication complexity between any two nodes toO(1), compared toO(logn) in existing tree-based approaches; 2) specialized role distribution among nodes at different layers to optimize workload and enhance system security; 3) an improved Borda counting method for efficient consensus node selection based on multiple attributes including verification rate, propagation rate, and storage space. The mechanism features dual middle-node communication paths with bottom nodes, providing enhanced fault tolerance and security compared to existing approaches. Experimental results demonstrate CTT's effectiveness in improving scalability and security while reducing communication overhead in consortium blockchain systems. The findings have the potential to significantly advance the performance and applicability of consortium blockchains in critical areas such as finance, supply chain, and healthcare.

Edge computing allows edge devices to offload computational tasks to edge servers, utilizing various hardware resources for efficient computation. Unlike cloud facilities, edge servers have limited resources. A long-term challenge is to quickly evaluate all the edge server resources and select the suitable server for the task, with high requirements for both processing time and allocation effect. The Edge Resource Allocation Problem (ERAP) represents a typical agent evaluation in collaborative work and falls within the realm of the Group Multi-Role Assignment (GMRA) problem. Based on the GMRA model, we formalize the ERAP as an optimization problem with an improved Edge- GMRA model. Additionally, we investigate the feasibility of an enhanced constraint scheme in the improved model. By boundary detection scheme, we implement quickly eliminated the infeasible solutions within the search range for ERAP. Experimental results demonstrate that the enhanced constraint scheme improves the allocation of high-priority tasks with superior acceleration as the number of agents increases, and the boundary detection scheme performs effectively in scenarios with insufficient server resources. The combination of these two schemes significantly accelerates the solution process, achieving an acceleration ratio exceeding 50%. The proposed dynamic adaptation mechanism with asynchronous agent monitoring and sliding-window threshold adjustment maintains the stability of the system under fluctuation of 15% resources, while our task-type recognition system demonstrates 92. 4% classification accuracy in six workload categories. Extensive evaluation shows the framework sustains sub-100ms decision latency during 80% resource contention scenarios, achieving 23% higher throughput than conventional methods while reducing service-level agreement violations by 41% in dynamic edge environments.

In the logistics chain, through collaboration, multiple supplier enterprises are able to achieve resource sharing, thereby offering a broader and more stable service scope while enhancing risk resilience. However, despite these benefits, differences in the distribution capabilities of various supplier enterprises can lead to inconsistencies in the overall service quality of distribution tasks. Therefore, in collaborative distribution, it is crucial to ensure the overall service quality while maximizing benefit. To address this challenge, this article formalizes the collaborative distribution problem (CDP) in the logistics chain via the environments — classes, agents, roles, groups, objects (E-CARGO) model. A novel solution is designed for CDP by extending the group multirole assignment (GMRA) model. This solution incorporates the qualification matrix adjustments (QMA) algorithm to systematically prioritize suppliers based on their qualifications, thereby maximizing benefit while ensuring the overall service quality of collaborative distribution tasks. Large-scale random experiments show that the proposed method effectively balances the tradeoff between the benefit and overall service quality in the CDP under various data distributions. Moreover, decision-makers can obtain an optimal assignment solution through the Pareto front.

In role-based collaboration (RBC) methodology, eliminating agent conflicts during the role assignment process is crucial for establishing a sustainable cooperative system. However, when agent resources are scarce, assignment strategies aimed at eliminating agent conflicts become infeasible. Consequently, there is a need to select the optimal assignment with a minimal number of agent conflicts, which is essentially a nonlinear bilevel optimization problem. To tackle this issue, we first design the group role assignment with minimized agent conflicts (GRAMAC) model to formalize this problem. It converts this problem into an extended integer linear programming (x-ILP) one and finds the optimal solution. Then, we prove that solving the GRAMAC model is an$\mathscr {NP} - \mathrm {complete}$task. Moreover, we identify the sufficient and necessary condition under which the GRAMAC model has the optimal conflict-free solution. Finally, extensive experiments demonstrate that, compared to existing strategies, our proposed method reduces the number of agent conflicts by an average of approximately 30% while ensuring the group performance of the collaborative system.

With the acceleration of globalization, the targeted international communication of national images contributes to enhancing a nation’s soft power and international recognition. It is challenging to select appropriate resources from the mass candidates for creating promotional works of national image. Existing research only focuses on the methodologies and lacks the systematic modeling and solving of national image resources recommendation. A collaborative recommendation approach to national image resources is proposed for targeted international communication. In it, the multidimensional features of national image resources and characteristics of communication audiences are modeled, and an evaluation mechanism is proposed to measure the comprehensive compatibility between national image resources and communication audiences. By innovatively introducing the role-based collaboration (RBC) theory and the environment-classes, agents, roles, groups, and objects (E-CARGO) model, the national image resources recommendation is formalized as a collaborative optimization problem. The mathematical model is built and solved via an optimization package. Finally, the case study and experiments show that the approach is efficient, feasible, and conducive to enhancing the efficiency of selecting national image resources. It offers a novel research paradigm for targeted international communication.

Role-based collaboration (RBC) is a novel problem-solving paradigm to facilitate collaboration. Group multirole assignment (GMRA), an extension of group role assignment (GRA), is a critical step in the RBC process, enabling the formation of efficient collaborative teams by reasonably assigning roles to agents. Recognized as a significant determinant impacting assignment and collaboration, conflict has been delineated and incorporated into GMRA. Nevertheless, the specified conflict is characterized as an oppositional conflict (OC), signifying that conflicting agents are engaged in conflict across the entirety of the role set. Rather than completely OC, which is highly specific, a more prevalent relationship involves conflict in certain aspects while remaining conflict-free in others. Therefore, we propose the concept of general conflict (GC) to describe the more common and realistic conflict relationship, offering a broader and novel perspective to depict conflict relationships. Then, we formalize these two problems by considering GC avoidance in GRA and GMRA, called GRA with GC (GRAGC) and GMRA with GC (GMRAGC), respectively. Furthermore, we establish the necessary conditions for the GRAGC and GMRAGC problems through a graph-theoretical lens, accompanied by a thorough analysis of their mathematical nature. Additionally, we propose practical solutions and refine methodologies to address both problems. The effectiveness of the improved algorithms utilizing necessary conditions is verified by simulations, which also provides evidence supporting the advantages of conflict avoidance.

This article investigates the almost sure consensus (ASC) problem for sampled-data multiagent systems (MASs) operating over semi-Markov switching networks (SMSNs) and facing different types of denial-of-service (DoS) attacks. During real-time information exchange among agents, communication failures between agents occur randomly, which may result in each possible network topology occurring with a certain probability, and its sojourn time is also stochastic. This necessitates the consideration of a more general switching signal to describe the stochastic switching phenomenon of networks. In pursuit of this goal, a semi-Markov chain is introduced to characterize the switching signal of stochastic interaction networks, whose sojourn time distribution allows for arbitrary continuous-time distribution and depends on the current and next state. Additionally, this article delves into the impact of two distinct types of DoS attacks on MASs. The first type involves random DoS attacks, which are also modeled by a semi-Markov chain to capture the stochastic nature of attack durations. The second type is deterministic DoS attacks, characterized by their frequency and duration. The proposed new stochastic analysis method, based on the law of large numbers, is used to analyze the ASC for MASs featuring SMSNs under the DoS attacks. The effectiveness of the proposed approach is demonstrated by evaluating the results obtained from two illustrative numerical examples.

Role-based collaboration (RBC) is an emerging and advanced methodology for problem-solving. A critical aspect of RBC theory is agent evaluation, which aims to assess agents’ abilities through a qualification value derived from a comprehensive analysis of their characteristics. This evaluation directly impacts the quality of role assignments. Existing research typically assumes that the qualification value is either predetermined, based on multiscale criteria, or following a predefined distribution. These assumptions, however, are overly idealistic and difficult to generalize, failing to capture the inherent volatility of the qualification value. To address this challenge, this article introduces a Wasserstein-based ambiguity set to model potential fluctuations in the qualification value, drawing on empirical distributions derived from historical sample data. Building upon the RBC framework and its abstract model environments, classes, agents, roles, groups, and objects (E-CARGO), we propose two data-driven models: distributionally robust group role assignment (DRGRA) and group multirole assignment (DRGMRA). These models aim to achieve more robust and optimal role assignments under uncertainty in agent evaluation. Leveraging strong duality, we reformulate DRGRA and DRGMRA as tractable finite mixed 0–1 convex problems, providing an approximation framework that reduces computational complexity. Notably, these models are adaptable to other problems with no uncertainty in agent evaluation, highlighting their modeling scalability. Experimental results demonstrate the effectiveness and robustness of the proposed models.

With the widespread application of AI in workplace scenarios, integrating AI into workflows has become a significant trend. However, most existing studies treat AI as independent agents operating in parallel with humans, assigning tasks in isolation, and failing to fully exploit AI’s impact on human capabilities. This article goes beyond the simplistic division of labor and proposes an AI-augmented collaborative task allocation method, emphasizing AI’s role in supporting human performance. By systematically modeling factors, including individual differences, interpersonal conflicts, technical constraints, and AI’s dynamic impact on human capabilities, we establish a multidimensional AI-augmented capability model to quantify capability impacts. Fuzzy interval numbers and cloud models are employed to address measurement instability and the heterogeneity of individual capabilities. Real-world case studies and numerical experiments validate the method’s effectiveness in scenarios that reflect realistic office characteristics and scales. Furthermore, experimental analyses identify transition patterns in AI-augmented environments, and verify the method’s adaptability to different AI development stages and diverse business contexts. The results provide a new theoretical perspective for understanding organizational resource reallocation driven by emerging technologies.

High-dimensional expensive optimization problems (HEOPs) have posed significant challenges to current surrogate-assisted differential evolution algorithms (SADEs) because of the curse of dimensionality. To enhance the optimization efficiency and solution accuracy for HEOPs, Surrogate-assisted differential evolution with search space tightening (SADE-SS) is proposed in this article. There are three main contributions in SADE-SS: first, a novel parameter adaptation strategy is incorporated into the framework of SADE to improve its scalability by leveraging information from approximated fitness values. Second, a search space tightening strategy is proposed to strengthen the local exploitation capacity by identifying promising local search spaces. Third, a switching strategy is proposed to manage the global and local surrogate-assisted searches, aiming to balance exploration and exploitation capacities. Experiments on expensive benchmark functions with dimensions ranging from 30 to 400 were conducted to verify the effectiveness of SADE-SS for HEOPs. Moreover, ablation experiments were conducted to validate each proposed component. Comprehensive experimental results demonstrate that SADE-SS can secure highly competitive performance over state-of-the-art SAEAs for HEOPs.

The audit process is a critical component of any company's financial management. It aims to guarantee that the financial statements of a company are accurate, reliable, and in compliance with relevant laws and regulations. Additionally, auditing provides an opportunity for companies to pinpoint areas for improvement, which can help increase efficiency and reduce instances of financial fraud and corruption. As lots of companies with small scales choose to perform the audits externally rather than relying on an internal team, the evaluation of the performance has become a significant problem during building an auditor team. Meanwhile, efficiency is another factor to pursue. This paper formalizes this problem with the Group Multirole Assignment with the Cooperation and Conflict Factors (GMRACCF) model. Specifically, we propose a new method to evaluate the efficiency factors and build a cooperation and conflict factors (CCF) matrix by turning it into time CCFs between auditors. Therefore, with several experiments being conducted, we can know that time factors can have different impacts on the performance of the External Auditor Assignment Problem (EAAP) depending on how much emphasis has been placed on its influence.

The development of the Internet has led to the rapid development of a new business model called crowdsourcing. However, increasingly complex crowdsourcing tasks are difficult to be decomposed and decoupled by the performers in the actual execution. The crowdsourcing platform needs to provide a detailed task assignment method to solve this problem. At the same time, crowdsourcing tasks may involve user privacy, and protecting private information from being known by others also needs to be considered by the platform. While completing the crowdsourcing task, considering task fairness and team fairness to improve the reliability of task completion and the fairness perception of crowdsourcing members. Therefore, this article formalizes the crowdsourcing team assignment problem through Environments – Classes, Agents, Roles, Groups, Objects (E-CARGO) model. Introducing information security constraints to construct a new sub-model (GRAINS) to solve the crowdsourcing team assignment problem. On the premise of obtaining the optimal performance assignment, the two types of fairness are discussed, providing a new decision-making scheme for the crowdsourcing platform. Through large-scale random simulation experiments, it is proved that the model can improve task and team fairness while ensuring the overall performance of the task, and quantitatively analyze the partial performance for fairness sacrificed.

Although public transportation brings more and more convenience and practicality, it also presents greater safety hazards and economic concerns. How to select reasonable bus driver rostering (RBDR) for bus companies has become a pivotal resource optimization issue in public transportation by extending the Group Multirole Assignment (GMRA) model, this paper formalizes such a problem. Moreover, we propose multi-criteria decision making as a new method for driver evaluation, incorporating agent capability and satisfaction as important criteria. Additionally, in order to find the result solution more reasonably, the parameter change rules are obtained through multiple simulations. We first use the slope to find the steep drop point, and then use the variance and range to find the balance point, thereby obtaining the target parameter combination. The staged parameter selection method improves operating efficiency. Large-scale simulations indicate that the improved GMRA algorithm is suitable for different scenarios and can return multiple parameter combinations. By using this method, bus companies are able to select optimal parameter combinations in order to make diversified decisions based on transportation resources and development strategies.

Everybody knows that China has put forward the concept of "carbon peaking and carbon neutrality" in response to climate change. This paper proposes a novel perspective from the standpoint of the Role-Based Collaboration (RBC), employing the Environment-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model and the Group Multirole Assignment (GMRA) model to construct an energy supply strategic planning (ESSP) that simulates the progressive carbon peaking process in order to compare and analyze the current carbon peaking target of China. Secondly, we propose a simple method to evaluate carbon peaking schemes, and conduct simulation experiments for different schemes. It is concluded that Chinese current carbon peaking target is relatively conservative, projecting achievement by 2027 with an estimated 19.7 billion tons of CO2 emissions, while having the lowest cost per unit of emission reduction. Furthermore, a significant reduction of 66.08% in carbon intensity is projected for 2030 compared to 2005, which is 1.08% higher than the original target.

Nowadays, a wide range of intelligent applications rely on deep neural networks (DNNs), ranging from face recognition to autonomous driving. Inference on pre-trained DNN is accurate and efficient, but resource-intensive, especially for end devices such as smartphone and wearable devices. To address the associated resource constraints, DNN inference tasks are often offloaded to the edge or cloud, achieved by partitioning the DNN and offloading part of the computation to another device. However, most of the existing solutions usually adopt more complex methods such as reinforcement learning and heuristic algorithms. In contrast, this paper introduces a simple approach by modeling the end-edge collaborative DNN inference system via the Environments - Classes, Agents, Roles, Groups, Objects (E-CARGO) model, and Role-Based Collaboration (RBC) methodology. A DNN partition point selection algorithm is proposed and the DNN task assignment problem in the system is formulated as a Group Multi-Role Assignment(GMRA) problem to be solved. Extensive simulation experiments demonstrate that the proposed solution can effectively reduce the global delay of DNN inference.

Blockchain technology has garnered increasing attention from researchers. Because blockchain systems may contain malicious or spatially limited nodes that may delay block verification and reduce block transmission rate, this work proposes a block transmission model by designing and using special clusters. This work proposes the cluster formation and selection mechanisms. Nodes are grouped into clusters in a blockchain, and clusters with high fitness values are chosen to transmit blocks by calculating their trust values and block transmission rates. The proposed method is compared with the peers: Layer-Chain, BlockP2P-EP and RNS. According to experimental findings, the proposed method is superior to its peers regarding the time needed for block synchronization and transmission, block occupation storage ratio, transaction throughput, and block transmission success ratio.

Most project development processes are iterative and can be divided into multiple tasks. One person can take on multiple tasks, and one task can be assigned to multiple people. The many-to-many personnel allocation method greatly improves the efficiency of the project and saves the cost of the project. There will be two different losses in this allocation plan: the tasks undertaken by personnel are too discrete and the personnel are easily distracted when undertaking important tasks. This paper first formally models the project personnel allocation problem through the Group Multi Role Assignment (GMRA) model. Then two new constraint formulas were proposed to extend the GMRA model to reduce the loss of personnel allocation and the necessary and sufficient conditions of the extended method were proved. Subsequently, two large-scale simulation experiments were carried out to compare and demonstrate the differences between the expanded new method and the original model, and to explore the sufficient and necessary conditions to increase the speed of finding feasible solutions for the new method. Using the improved model for arranging personnel of development projects not only enables efficient many-to-many allocation but also helps reduce a lot of hidden losses in the project process.

Courier delivery is the end of the supply chain and affects the final delivery of products. Due to the promulgation of new courier delivery regulations, home delivery services have become the main choice for consumers, which has resulted in a greater workload. How to Reasonably Allocate Couriers for LEan Management (RACLEM) in supply chain optimization poses a challenge to traditional logistics. By extending the Group Multirole Assignment with Efficiency Degradation (GMRAED), this paper formalizes the problem. Moreover, we propose a quantitative calculation method of efficiency degradation based on Amdahl's law, taking region similarity and the number of tasks as important criteria, and compare the performance of different personnel arrangements. Additionally, compared with the brute force algorithm, we combine GMRAED and Genetic Algorithm (GA) to create a practical solution. Large-scale simulation experiments demonstrate that the genetic algorithm significantly shortens the solution time, and the lowest experimental accuracy is 99.407%. By using the above method, decision-makers are able to assist companies optimize personnel resource allocation, minimize personnel waste, and improve the performance of courier delivery within a shorter timeframe.

In addition to prompt garbage collection and strategic routing, effective waste management also involves optimizing resource allocation and ensuring the sustainability of disposal practices. The E-CARGO (Environments – Classes, Agents, Roles, Groups, and Objects) model, coupled with the Role-Based Collaboration (RBC) methodology, offers a sophisticated approach to address these multifaceted challenges. By simulating the scheduling of garbage trucks for the transportation of bins within urban environments, the model provides valuable insights into the dynamics of waste collection operations. Furthermore, by integrating RBC, which dynamically assigns roles to agents involved in the waste management process, the model promotes coordination and cooperation among various entities, including municipal authorities, waste collection agencies, and residents. Through this adaptive framework, the article proposes a holistic approach to urban waste management that not only enhances operational efficiency but also fosters environmental sustainability and community engagement.

The Bank Credit Decision-Making Problem (BCDMP) is one of the main issues that bank operations need to face. To obtain the maximum profit value and optimal loan plan of the bank as much as possible, this article suggests converting BCDMP into a Many-to-Many Assignment Problem, which can be specified by the Multi-Role Assignment (GMRA). GMRA is a sub-model of the E-CARGO. By revised GMRA, the relationship between the enterprises and loans is converted into the relationship between agents and roles, and a multi-dimensional and multi-index evaluation method is used to evaluate the matching degrees between enterprises and loans. We use the Entropy Weight Method (EWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to obtain the enterprises' score for a loan through four indicators: profits, inventory, turnover ability, and credit. Then, we considered the impact of different loan interest rates on bank profits, obtained enterprise scores under different loan interest rates, and used linear programming to solve the problem, achieving good results (The bank achieved a profit margin of 5.43176% via revised GMRA).

Quasi group role assignment (QGRA) presents a novel social computing model designed to address the burgeoning domain of self-service spatiotemporal crowdsourcing (SSC), specifically for tackling the photographing to make money problem (PMMP). Nevertheless, the application of QGRA in practical scenarios encounters a significant bottleneck. QGRA provides optimal assignment strategies under conditions where both the number of crowdsourced tasks and workers remain stable. However, real-world crowdsourcing applications may necessitate the phased integration of new tasks. With the rapid increase in the number of tasks, a set of residual tasks inevitably exists that are difficult to complete. To maximize the completion of crowdsourced tasks, workers may be assigned low-yield or even unprofitable tasks. Given the reluctance of crowdsourcing workers to be overstretched for these tasks, along with the inherent characteristics of self-service crowdsourcing tasks, this can lead to the failure of the assignment scheme. To tackle the identified challenges, this article proposes the QGRA with agent satisfaction (QGRAAS) method. Initially, it sheds light on a creative satisfaction filtering algorithm (SFA), which is engineered to perform optimal task assignments while actively optimizing the profitability of crowdsourcing workers. This approach ensures the satisfaction of workers, thereby fostering their loyalty to the platform. Concurrently, in response to the phased changes in the crowdsourcing environment, this article incorporates the concept of bonus incentives. This aids decision-makers in achieving a tradeoff between the operational costs and task completion rates. The robustness and practicality of the proposed solutions are confirmed through simulation experiments.

Role negotiation (RN) is situated at the initial stage of the role-based collaboration (RBC) methodology and is independent of the subsequent agent evaluation and role assignment (RA) processes. RN is to determine the roles and the resource requirements for each role. In existing RBC-related research, RN is assumed to be static. This means that the roles and the resource requirements for each role are predetermined by decision-makers. However, the resources allocated to each role can vary. At this time, iterative RN outcomes will have different RA results. There may not be a direct dominant relationship between different RA outcomes, especially when solving group role assignment (GRA) with multiple objectives (GRA++) problems, which makes it even more complex. To address these concerns, we introduce the original bilevel GRA++ (BGRA++) model. Specifically, at the lower level of BGRA++, a strategy is designed for quantifying iterative RNs. For the upper level, we introduce the novel GRA-NSGA-II algorithm for the RA process. Finally, we introduce the concept of decision tolerance to assist decision-makers in selecting the optimal solution from the multiple RNs. Last, simulation experiments are conducted to verify the robustness and practicability of the proposed method. Comparisons and discussions show that the proposed solution is highly competitive for solving the GRA++ problem with iterative RN.

In collaborative endeavors, there are always various types of cooperation, conflict, and public interest relationships. Taking home appliance production as a typical example, this article extends the group role assignment with the cooperation and conflict factors (GRACCF) model by newly adding public interest relationships to formalize the problem dealing with the above three collaboration relationships. By adding three different threshold parameters to represent cooperation, conflict as well as public interest relationships, we can make complex decisions based on the benefit values from different relationships. Through the discussion and analysis of large-scale experiments, different change trends are presented to help make certain adjustments and provide suggestions on the boundaries of these values in production and formulate appropriate production plans after determining these boundaries.

With the massive popularization of e-learning, collaborative learning via learning teams has become indispensable to enhancing the learning efficiency and learning quality of overall learners. The team leader usually plays a key role in collaborative learning. However, the existing research ignores the key characteristics of learners and constraints relevant to e-learners when identifying appropriate team leaders and compatible members. A novel collaborative optimization approach to learning team formation is proposed based on a refined learner model and the environments—classes, agents, roles, groups, and objects (E-CARGO) model. With the proposed approach, a learner is modeled by combining 5-D characteristics (i.e., cognitive ability, leadership, sociability, learning style, and personality) and three types of constraints (e.g., conflicts, genders, and the number of members), and an assessment mechanism is designed to measure the comprehensive abilities of learners for identifying an ideal team leader and selecting the team members for a team. By innovatively introducing the role-based collaboration theory and E-CARGO model, the leader-centered learning team formation problem is formalized as a collaborative optimization problem. The mathematical model and the constraint relations are established for this problem, which is solved based on the IBM CPLEX package. Finally, a case study and experiments demonstrate that the proposed approach is efficient and feasible, in favor of improving the satisfaction degree of learners.

Recently, the road trip in regional central cities has become one of the mainstream travel styles for Chinese tourists. However, existing research fails to plan the road trip route in Chinese regional central cities due to its flexibility, complexity, and long-term characteristic. A collaborative route planning approach for road trips, namely CoRPoRT, is proposed. This approach decomposes the route planning of a road trip into two role-based collaboration subproblems to alleviate the complexity issue. Then, the environments–classes, agents, roles, groups, and objects (E-CARGO) model is innovatively employed to formalize the problems and guide the optimization process for dealing with the long-term characteristic of road trips. Moreover, we identify the complex constraints affecting point of interest selections and propose an efficient solution via the IBM CPLEX solver to handle the flexibility of road trips. Finally, a case study and simulation experiments verify the effectiveness of CoRPoRT. CoRPoRT presents a general problem modeling method and a novel research paradigm for the route planning of road trips.

Blockchain as a ledger technology is attractive without the need for central servers. There are many types of blockchains in different fields, such as digital asset trading and payment, which have business interactions. In the fields, the digital asset and payment information on their blockchains need to be securely cooperative with each other. However, some business activities are on different blockchains, which have different consensus algorithms and network architectures, thus limiting the interoperability among these activities and making each blockchain an island. Cross-chain technology can connect different blockchains and realize the interoperability and sharing of information among them. This work designs a cross-chain digital asset system for secure trading and payment. It builds two parallel chains, i.e., digital asset chain (DAC) and payment chain (PC), and their functions are analyzed and designed. The cross-chain, i.e., relay chain (RC), is used to realize cross-chain interoperability, where the cross-chain message format and authority setting are designed to endow parallel chains with the ability to recognize. The decentralized characteristic of the RC allows cross-chain messages to be safely transmitted to ensure secure trading and payment. Through testing and analysis, the proposed system can provide more secure trading and payment than its peers.

Based on role-based collaboration (RBC), group role assignment (GRA) optimizes a team’s overall performance by assigning the most appropriate individual agents from the team’s viewpoint based on agents’ role-playing abilities. As an extension of GRA, GRA with a training plan (GRATP) deals with the impact of training on team management. Considering the correlation between roles, the training of one agent on one role also affects the performance of the agent in other roles. Moreover, in the adaptive collaboration (AC) problem, the training time also affects significantly the agent’s ability, as an agent’s ability changes over time. However, the existing GRATP models fail to consider these factors in the collaboration process. Therefore, we aim to address the role-correlation-based adaptive GRATP (RCA-GRATP) in this article. This article contributes two aspects to the literature on AC. 1) RCA-GRATP problem is abstracted based on RBC and GRA. To the best of the authors’ knowledge, this is the first article that explicitly considers role correlation in the RBC problems. 2) A comprehensive formalization of RCA-GRATP and two solving algorithms for diverse situations are proposed to solve the formalized problems. Experiments are carried out to verify the effectiveness of the proposed algorithms in diverse scenarios.

The educational equality or equity problem (EEEP) has a long-term debate in civilization. This problem is often discussed by sociologists, philosophers, educationists, and so on for various phenomena or decisions. However, there is hardly any absolutely convincing answer to this question. We believe that a series of quantitative methods may provide decision-makers with a clearer orientation in their discussions and help them make decisions. Therefore, this article depicts a common scenario to discuss the EEEP. It uses quantitative methods to conduct computational social simulations and obtain the results under equal or equitable conditions via role-base collaboration (RBC), environments-classes, agents, roles, groups, and objects (E-CARGO), and group role assignment (GRA). The simulation results show that educational equality or equity can be significantly improved by making a reasonable and optimized allocation plan through GRA. Finding the optimal allocation plan, which validates the reliability of the results by changing the experimental parameters of equality and equity, is helpful and interesting to relevant decision-makers.

Predicting quality of service (QoS) is an important issue in the field of service recommendation that has been widely studied in the past few years. Many current methods predict QoS values based on the historical invocation records of services, but most of them ignore the time-varying characteristics of these values. Capturing time-varying characteristics to ensure accurate prediction of QoS values has become a key problem in the area. To solve this problem, in this article, we first apply probabilistic matrix factorization in QoS time series of sparse QoS matrices to extract time-varying feature series of users and services. Then we construct a gated feature extraction network (GFEN) to compensate for feature loss due to matrix factorization and enrich the limited information due to the sparsity of QoS matrices, where the heart of GFEN is an enhanced gated recurrent unit (EGRU) and a generative adversarial network is proposed to train GFEN. Extensive experimental results show that the proposed model outperforms state-of-the-art methods in terms of QoS prediction accuracy.

Quality of Service (QoS) prediction is a crucial issue in service recommendation, which has been widely studied in the past few years. It faces several challenges, including improving QoS prediction accuracy. Can one extract and use deep features of users and services to improve it? This work answers this question by proposing a deep-learning model for service QoS prediction. In this model, a feature mapping and inference network is first designed to obtain high-dimensional feature matrices of users and services, which can enhance data flow information and reflect the deep relationships among users and services. Then, feature compensation blocks are designed to compensate for the possible loss of feature information in feature mapping and inference. Finally, a QoS prediction network is constructed to fuse the obtained feature matrices to predict QoS values. Experimental results show that the proposed method can achieve higher prediction accuracy than ten typical and representative methods, thus advancing the state of the art in QoS prediction.

In situations involving teams of diverse robots, assigning appropriate roles to each robot and evaluating their performance is crucial. These roles define the specific characteristics of a robot within a given context. The stream of actions exhibited by a robot based on its assigned role are referred to as the process role. Our research addresses the depiction of process roles using a multivariate probabilistic function. The main aim of this study is to develop a role engine for collaborative multirobot systems and optimize the behavior of the robots. The role engine is designed to assign suitable roles to each robot, generate approximately optimal process roles, update them on time, and identify instances of robot malfunction or trigger replanning when necessary. The environment considered is dynamic, involving obstacles and other agents. The role engine operates hybrid, with central initiation and decentralized action, and assigns unlabeled roles to agents. We employ the Gaussian process (GP) inference method to optimize process roles based on local constraints and constraints related to other agents. Furthermore, we propose an innovative approach that utilizes the environment’s skeleton to address initialization and feasibility evaluation challenges. We successfully demonstrated the proposed approach’s feasibility, and efficiency through simulation studies and real-world experiments involving diverse mobile robots.

Role-based collaboration (RBC) is a new problem-solving paradigm that uses model environments-classes, agents, roles, groups, and objects (E-CARGO) to facilitate modeling. Task decomposition is widely adopted to reduce the difficulty of execution, resulting in multigroup collaboration problems. Multigroup role assignment (MGRA) has been proposed to solve the assignment of multiple groups. In many actual scenarios, there are dependencies between the decomposed subtasks, which is neglected by existing MGRA methods. Moreover, they disregard the fact that subtasks have diverse significance in the development of a project, which is of paramount importance to ensure the proper allocation of resources. To solve the complicated problem, the structured subtasks are formalized based on the emerging and promising RBC theory and E-CARGO model. Then, the assignment is abstracted into a complicated single-objective multiconstraint problem, named adaptive-equalized MGRA (AE-MGRA). In the formulated AE-MGRA problem, to improve the utilization of limited resources, the performance of each E-CARGO group needs to be equalized according to the corresponding subtask’s weight. As the optimal solution is difficult and time consuming to obtain, a tolerable deviation is utilized to achieve a near-optimal solution. Extensive experiments are conducted to sufficiently demonstrate the efficiency and stability of the proposed practical solution. In addition, the experimental results on static assignment and dynamic assignment further prove the effectiveness of the solution.

In the production process of automobiles, parts procurement is invariably a crucial step. In order to find an optimal decision, it is a challenge to match parts to suppliers for the limited financial and material capabilities of every supplier. This paper formalized the problem by Group Multirole Assignment (GMRA). Meanwhile, the success of this assignment process depends on the choice of the agent evaluation method. It depends on the industrial chain data, which can acquire feature indexes of parts from previous purchase records. Furthermore, comprehensive evaluation of parts procurement bases on multiple factors. Thus, it is difficult to reflect different quantifications using the multifactorial parameter semantics. Therefore, we propose a new method of Fuzzy Hierarchy Comprehensive Evaluation (FHCE), using membership grades of the fuzzy theory to differentiate the parameter and the weight, which can use objective quantitative analysis to optimize procurement plan. After that, based on GMRA, decision makers are able to maximize the resource utilization ratio to determine optimized solutions when funds or part types are limited. Simulation experiments indicate that the proposed method is efficient and feasible, which is verified practicable.

New employee training scheduling is one of the most common events in many enterprises. Solving this problem has its significance and is useful in daily administrations and operations. Group Role Assignment (GRA) model is widely applied in the assignment problem. However, there are still many challenges to applying the GRA model. For example, when we need to assign different jobs for the same person at different times, GRA needs more structures to specify constraints. If we use the strategy that combines the time factor with the agents or roles to formalize new agents or roles, the problem can be converted to a solvable GRA problem with constraints. The focus of this article is to give a practical solution to this kind of problem by using the GRA formulations in expressing constraints. The formalization makes us resolve the problem easily through integer programming (IP) with the PuLP package of Python. Large-scale simulation experiments demonstrate the practicability and robustness of our method.

The phenomenon of uneven product quality in software outsourcing enterprises is common. Even for products with similar functionality, the company cannot guarantee the final delivery quality. Effectively solve the stability of product quality, which is more conducive to long-term cooperation between customers and the company. Hence, this paper formalizes the Outsourced Products Task Allocation (OPTA) problem via the Environments-Classes, Agents, Roles, Groups, Objects (E-CARGO) model. Through its sub model Group Multirole Assignment (GMRA), a team with the optimal qualification value can be obtained. However, the stability of the assignment result is still not guaranteed. Therefore, this paper innovatively introduces the concept of variance and proposes the Group Multiple Role Assignment with Considering Variance (GMRACV) model to tackle this issue. And make better improvements to it, achieving a performance loss of 1% in exchange for about 32% stability. Large-scale randomized experiments show that the proposed model can effectively reduce the variance between products while maintaining the overall quality of all products. And for data with different distributions, the model can still obtain excellent results stably, which further verifies the feasibility of the model.

Trust evaluation and trust establishment play crucial roles in the management of trust within a multi-agent system. When it comes to collaboration systems, trust becomes directly linked to the specific roles performed by agents. The Role-Based Collaboration (RBC) methodology serves as a framework for assigning roles that facilitate agent collaboration. Within this context, the behavior of an agent with respect to a role is referred to as a process role. This research paper introduces a role engine that incorporates a trust establishment algorithm aimed at identifying optimal and reliable process roles. In our study, we define trust as a continuous value ranging from 0 to 1. To optimize trustworthy process roles, we have developed a consensus-based Gaussian Process Factor Graph (GPFG) tool. Our simulations and experiments validate the feasibility and efficiency of our proposed approach with autonomous robots in unsignalized intersections and narrow hallways.

Although cloud manufacturing offers greater flexibility and diversity than traditional manufacturing, it also presents greater uncertainty and variability. How to select stable cloud providers for material procurement (SSCPFMP) has become a critical supply chain optimization issue in cloud manufacturing. By extending the Group Role Assignment (GRA) model, this paper formalizes the problem. Moreover, we propose a new method for evaluating cloud providers that incorporates stability as an important criterion. Additionally, in order to complete the stability assessment as quickly as possible, we propose using the KB4 logic instead of the commonly used KB5 to mine potential cooperative relationships between cloud providers. We prove by deduction that KB4 and KB5 are equivalent. Largescale simulation experiments indicate that the KB4 logic performs significantly better than the KB5 logic, which can be improved by up to 43.78%. By using this method, decision makers are able to find more stable cloud providers for material procurement within a shorter timeframe.

With the rise in human activities, the trend of increasing carbon emissions is becoming more apparent. Enterprises are also facing serious challenges in the trend of low carbon and environmental protection, and the procurement process has a significant impact on carbon emissions. In this paper, we propose a procurement solution that integrates many aspects of procurement factors while focusing on reducing carbon emissions. Specifically, we first assess the cost of procurement, the carbon emissions involved, and the quality of the items. We then aggregate the evaluated data and combine them using the analytic hierarchy process to calculate the total qualification value. Also, we specified various constraints and used the E-CARGO model to formalize this problem. Based on the qualification values, we can use the IBM ILOG CPLEX Optimization (CPLEX) package to find the best sourcing solution according to the working hour allocation. In our experiments, our proposed solution can effectively derive the optimal procurement solution based on the purchaser's needs while focusing on reducing the carbon emissions involved in the procurement.

In the team allocation problem (TAP), eliminating team disparities aims at keeping an equilibrium of the resource or ability among teams for equality. For this concern, existing literature merely utilized the golden mean principle to eliminate team disparities from a static perspective. Few of them reasonably investigate the pros and cons of this principle from a computational perspective. Moreover, maintaining equilibrium is a dynamic process and requires dynamic adjustment, especially after considering team members’ self-efforts and adaptivity. With respect to the environments—classes, agents, roles, groups, and objects (E-CARGO) model and its role-based collaboration (RBC) methodology, this article formalizes and solves the TAP, i.e., revised group role assignment (GRA) problem, from both the individual and team’s perspective. Based on the revised GRA, this article provides novel insight into the effectiveness of dynamically maintaining equilibrium, which may help decision-makers be proactive in building more sustainable teams. Relevant large-scale simulation experiments are conducted in this article to verify the proposed method. This article reveals a social paradox: even though considering all about the team members’ self-efforts and adaptivity, equilibrium still seems inequitable. Conversely, pursuing equilibrium may bring the Matthew effect.

The World Bank estimates that the number of refugees worldwide will reach 140 million by 2050 due to global warming and local wars. Considering the rapid increase in the number of refugees, an efficient and feasible assignment method is required for refugee resettlement. This article formalizes the refugee resettlement issue using the Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model. A novel solution is designed for Refugee reSettling (RS) by extending the Group MultiRole Assignment (GMRA), which applies the agent stability evaluation method as a feedback mechanism while optimally resettling refugees. With this proposed solution, decision-makers can swiftly resettle refugees from multiple suffering countries while appropriately ensuring host countries’ benefit. Finally, large-scale simulation experiments based on the Python PuLP platform are carried out to demonstrate the practicability and robustness of the proposed solution. The simulation results provide a solid decision-making reference for the leaders of the world.

Group role assignment with cooperation and conflict factors (GRACCFs) is a creative social computing method for team establishment. It can maximize the new team’s performance through role assignment considering potential cooperation or conflict factors among agents. However, this method has two bottlenecks in practical applications. First, in the scenario of establishing a new team from several existing teams, collecting the pertinent cooperation or conflict information encounters challenges. Second, GRACCF merely takes the CCFs as a part of the objective function for team performance, but this will underestimate the CCFs’ impacts on the sustainable development of the team. This article tackles these issues by extending GRACCF from a new viewpoint. It first designs a KD45 logic algorithm based on the KD45 logic system, which can discover the implicit cognitive CCFs through logical inferences with closure calculations. Then, it proposes an original team evaluation method that can help decision-makers determine the weights of team performance and CCFs’ impacts based on their demands. Large-scale simulation experiments indicate that the proposed solution is practicable and robust. The proposed method provides a solid decision-making reference for administrators when establishing a sustainable team.

In mass crowdsourcing, a platform is used for task allocation. Large complex tasks may be assigned to teams directly. To ensure the rapid accomplishment of tasks, we need to assign a task to multiple teams, while a team is composed of at least one worker. On the other hand, in order to ensure the enthusiasm and income of workers, the platform allows one worker to participate in a limited number of teams. Thereby, a team may undertake multiple but limited tasks. It is obvious that task allocation needs to avoid each worker being overloaded as well as prevent information leakage, which is caused by a task assigned to different teams at the same time. Therefore, different teams being assigned to a certain task cannot include the same workers. Such a scenario forms a many-to-many (M2M) assignment problem [or group multirole assignment (GMRA)] under high-order cardinality (HC) constraints, while the platform wants to choose appropriate and excellent teams for all the tasks in a specific time window. In order to solve this problem, this article studies and formalizes the above problem by introducing HC and conflicting agents on roles (CAR) constraints to GMRA. The main contributions of this article include: 1) the first formalization of the team allocation problem (TAP) in crowdsourcing through extending GMRA and the creation of a composition matrix to express HC constraints of agents and conflict avoiding constraints; 2) theoretical proofs of the theorems of the formalized problem, such as a necessary and sufficient condition (NSC), which confines the solution space of the problem; and 3) a practice solution to the proposed problem based on the IBM ILOG CPLEX optimization package (CPLEX). All the proposed approaches are verified by simulation experiments, which demonstrates that the proposed approaches are efficient, feasible, and practicable.

Role-based collaboration (RBC) is an adaptive computational methodology that uses roles as underlying mechanisms to facilitate and analyze system behavior for entities that collaborate and coordinate their activities with or within these systems. In dynamic environments, including those that occur in large-scale simulations, visualization provides insights into complex systems behaviors. This article presents a visual analytics (VA) approach to studying dynamics involved in adaptive collaboration (AC) for large, multiagent simulation model using new open-source tools. The results show that time-varying systems can be steered for optimal performance and assessing adaptations using VA dashboards.

Pareto optimality refers to a benefit distribution that cannot be improved without lowering the benefit of at least one individual. Group role assignment (GRA) pursues an optimal role assignment from the team’s viewpoint and mainly considers the team performance. It is valuable to check whether an optimal assignment is consistent with the Pareto optimality. Otherwise, there would be discomforts in social development. In this article, we establish a model for simulating a resource/benefit distribution by using the environment–class, agent, role, group, and object (E-CARGO) model, which was developed by our previous work. To obtain a deep understanding of the Pareto optimality, we use a revised GRA model to check for possible Pareto improvement. This article contributes a unique and novel way to investigate economic or social phenomena with the E-CARGO and GRA model, provides further insight into the Pareto optimality from the perspective of role assignment, and confirms the feasibility of Pareto improvements. The proposed algorithm is verified using theoretical proofs and simulation experiments. Interesting findings are deduced and presented: 1) Pareto improvements do not exist when resources are insufficient and are feasible only when the number of provisions is larger than that of requests. 2) Pareto assignment exists if and only if we have sufficient people for production and sufficient resources for consumption. The defined Pareto GRA problem seems complex but we can solve it with a simple and efficient algorithm. 3) Overproduction is driven by not only individual requests but also the social requirement of Pareto improvements.

This article systematically establishes a new category of assignment problems by reviewing and extending the problems related to group role assignment (GRA) from a novel vision. After reviewing seven related GRA with Constraints (GRA+) problems, this article specifies three new major assignment problems to make GRA+ problems complete and coherent. In addition, this article proves a series of new related theorems, proposes new conditions for the specified problems to have feasible solutions, and verifies the hardness of the newly specified problems. This article finally verifies the value of the presented theoretical work and provides a generalized formalization of this category of problems, i.e., the one highly abstract optimization problem, which is specified the first time. This article contributes to the literature of assignment problems with a novel category of well-defined problems, i.e., GRA+. The presented problem category is original, consistent, but far from complete. It will initiate further innovations in assignment research along with the presented directions. This article again demonstrates the power of the methodology role-based collaboration (RBC), and the environments—classes, agents, roles, groups, and objects (E-CARGO) model.

Training is an effective way to improve agents’ performance. As an extension of group role assignment (GRA), GRA with a training plan (GRATP) aims to maximize the group performance or benefit by finding the optimal role assignment and training plan. However, GRATP has only been discussed in static scenarios. In dynamic environments, agents’ performance changes over time and adaptive collaboration is designed to keep the group in a good state. Therefore, this paper investigates the GRATP problem in adaptive collaboration. The timing of training has a significant impact on the improvement of individual performance, which will in turn affect the team performance and total benefit. By utilizing Role-Based Collaboration and GRA, the optimal training timing and training plan are obtained, which maximize the total benefit of the group. After training, the roles are re-assigned to the agents based on their current performance. This paper’s contributions include formalizing the GRATP problem in adaptive collaboration and presenting a solution to it. The effectiveness of the proposed method is verified by experiments.

In order to solve the problem of the task collaboration of multi-agent, we propose a task collaboration planning based on mentorship mode (MM-TCP). In this paper, the Group Role Assignment (GRA) framework is used to formalize the multi-agent task collaboration problem. The Growth curve is adopted to describe the change trend of the qualification values of the prior and posterior agents in a mentorship mode. The IBM ILOG CPLEX optimization package (CPLEX) is used to find the solution of the model. Experiments show that the proposed method is feasible and effective. With the utilization of a mentorship mode, the performance of the whole team is improved. In addition, the effect of the Growth curve model on team performance is also discussed in this paper.

Timing hot gas defrosting is one of the commonly used methods of cold storage defrosting, it only needs to determine the time period interval and duration. However, this method is difficult to arrange a reasonable defrost time period according to the different power of the air cooler and the type of industrial power time period, resulting in a waste of electricity. Thereby, this paper tackle this issue using the Group Role Assignment (GRA) model to formalize the defrosting period allocation with flexible intervals problem (DPFI) of the air cooler. On the one hand, it innovatively uses periods instead of discrete individuals as agents for the allocation of defrosting periods. On the other hand, according to the actual frosting situation of the air cooler, different period intervals are adopted to defrost the air cooler. The focus of this article is to express the defrosting period interval constraint linearly. The formalization of GRA makes it easy to find a solution through integer programming (IP) using the PuLP package of Python. Large-scale simulation experiments verify the robustness and practicability of the proposed method. In addition, by comparing the power cost of defrosting at different period intervals and the same period interval, simulation experiments show that using flexible period intervals to defrost the air cooler can significantly reduce the power cost of defrosting.

Different new employees often have different wishes to take part in different departments. Personal wishes are a significant issue in human resources management. Decision makers need to not only assess employees’ job skills, but also consider their personalized choices with respect to the job posts. How to balance the workers’ wishes with the group performance is a challenge. This paper formalizes this problem via the Group Role Assignment with Balance (GRAB). Through a compensatory assignment based on GRAB, an interesting and successful approach is proposed, which can balance wishes and performance in a state of equilibrium. Simulation experiments indicate that, the proposed method is efficient and feasible, which is verified practicable.

To make full use of the resources in production, the orders are usually split, decoupled, and reassembled to series new orders, which often lead to a new complex high-order set of tasks. Traditional processing methods are to first produce some portion of the order, and then manually adjust the rest production plan. Such a method may cause over-production. Also, when tasks are urgent, it is difficult to control the waste rate of emergency raw materials due to quick responses to the urgent requests without careful planning. It is not a trivial work to guarantee efficiency and emergency of production in this case. Therefore, this paper formalizes the high-order set assignment problems (HOTP) by using group role assignment (GRA). Based on GRA, this paper proposes a role negotiation method by using the Hierarchical clustering and Analytic Hierarchy Process (AHP) algorithms. The formalization of HOTP makes it easy to find a solution using the IBM ILOG CPLEX optimization package (CPLEX). Based on the proposed method, decision makers can optimize production resources and assign them in place at one time, minimize the waste and ensure the productivity. The proposed approaches are verified by simulation experiments and demonstrated to be efficient, reasonable and practicable.

In safety-critical applications, where several mobile robots and autonomous agents are being utilized for a mission, a fault-resilience behavior of the system is necessary. The fault resilience mechanism mostly uses the robot’s redundancy and tasks reassignment to recover malfunctioning and increase operating efficiency. The E-CARGO (Environments - Classes, Agents, Roles, Groups, and Objects) model designed for the Role-Based Collaboration (RBC) approach has been used successfully on cooperative Multi-Robot Systems (MRSs). Role-based characteristics of E-CARGO will facilitate cooperative decision-making and simplify handling failure. This paper develops an extended E-CARGO model for a fault resilience role engine. Agents use factor graphs to update the process role and manage the potential failure in each time step. We apply hybrid control in this paper. By “hybrid” we mean that evaluating and assigning initial roles are centralized, and role-playing is decentralized based on the local observations. The RBC life cycle and a Bayesian consensus will maintain fault resilience behaviors. Potential failure can be identified in a Bayesian way by updating agents’ reliability and calling the central unit to assign new process roles to guarantee robustness. Simulation experiments show that the proposed role engine can increase performance and tolerate failures in multi-robot path planning scenarios.

In many practical cases, the original task is divided into smaller, easier-to-complete tasks and assigned to different groups. Group role assignment (GRA) is dedicated to optimizing the performance of a group, which is not applicable to the multi-group role assignment (MGRA). Moreover, in dynamic scenes, the agents’ capabilities change over time, further complicating the problem. Based on the emerging and promising role-based collaboration (RBC) theory and its E-CARGO (Environments - Classes, Agents, Roles, Groups, and Objects) model, we formulate the adaptive MGRA problem, and propose a novel current state-based MGRA (CSB-MGRA) algorithm to keep the entire team productive. The constraints of the tasks are not the same due to their diverse characteristics and needs. Moreover, team members do not necessarily remain the same in the whole process, and staff transfers may occur between groups. A constant assignment scheme is not guaranteed to maximize team performance. Therefore, the constraints of different groups are set to be different, and re-assignments of the whole team are considered in the construction of CSB_MGRA. The experimental results prove the practicality of the solution proposed in this paper.

With the frequent occurrence of disasters, such as war, storms, hurricanes, and earthquakes, post-disaster rescue is particularly important. Based on the principle of life first life detection and rescue have become the primary task of post-disaster rescue. In this paper, the E-CARGO (Environments – Classes, Agents, Roles, Groups, and Objects) model is used to formalize the life detection and rescue problem. With the help of the idea of the convex hull algorithm, a Hierarchical Coverage Based on Square Algorithm (HCBS) is proposed to achieve the maximum coverage of the rescue area with the minimum number of detection areas, and then a rapid assignment of UAV life detection and rescue with minimized rescue cost is realized through group role assignment (GRA). Using the PuLP extension library of Python, we implement the proposed algorithm. The experiments show that the proposed method is fast and effective.

Self-service spatiotemporal crowdsourcing (SSC), a booming variant of spatiotemporal crowdsourcing (SC), emerges because of the vigorous development of the mobile Internet. Unlike the conventional SCs, the particularity of self-service in SSC may lead to unfinished tasks at the end of the entire assignment process, making a one-time assignment scheme ineffective. SSC is essentially an adaptive collaboration (AC) problem that requires a dynamic assignment strategy for a higher task completion rate. This article tackles this issue by establishing a quasi group role assignment (QGRA) based on a typical SSC scenario, that is, the photographing to make money problem (PMMP). First, it sheds light on a novel role awareness method, which can effectively divide tasks to accelerate the solution while, to some extent, raising the task completion rate. Second, it specifies an agent satisfaction evaluation (ASE) method to quantify the relationship between task completion rate and workers’ satisfaction. This method aims at considerably ameliorating task completion rate. Last, it extends QGRA with a new AC algorithm, which can achieve AC of the workers while accomplishing the crowdsourcing task. Moreover, utilizing the ASE method can help decision-makers balance the task completion rate and the workers’ satisfaction. Large-scale simulation experiments based on the real crowdsourced datasets exemplify the robustness and practicability of the proposed solutions. This article contributes a new version of the group role assignment (GRA) model, that is, quasi GRA (QGRA), a creative formalization to solve the AC problem.

The ever-increasing popularity of recommendation systems allows users to find appropriate services without excessive effort. However, due to the unstable and complex network environment, the historical behavior data of users are quite sparse in most cases. The inherent drawbacks render preference prediction infeasible for cold-start users and have become a crucial issue to be resolved in recommendation systems. To deal with the problems, we first present a Trust-based Collaborative Filtering (TbCF) algorithm to perform basic rating prediction in a manner consistent with the existing CF methods. Then, we propose the Hybrid Collaborative Filtering Recommendation approach with User-Item-Trust Records ($\text {UIT}_{\text {hybrid}}$), a novel approach that incorporates user trust into the existing CF-based methods in a harmonious way to supplement rating information.$\text {UIT}_{\text {hybrid}}$employs multiple perspectives to extract proper services and achieves a good tradeoff between the robustness, accuracy, and diversity of the recommendation. We conduct extensive real-world experiments on the Epinions data set to demonstrate the feasibility and efficiency of$\text {UIT}_{\text {hybrid}}$.

The Pareto 80/20 principle is extensively cited in discussing social distribution and is usually applied to explain phenomena in economics. However, little in the literature investigates the driving force of such phenomena. The known driving force may help decision makers be proactive in administering a society before it becomes unsustainable. The environments-classes, agents, roles, groups, and objects (E-CARGO) model and the role-based collaboration (RBC) methodology assist the formalization of group role assignment (GRA) problem, which models and solves the optimization problem for a group of agents to play a set of roles from the team’s perspective. Based on GRA, this article proposes a new way to investigate social development/distribution problems, such as the Pareto 80/20 principle, with computational social simulations. The proposed method is verified by experiments. This article reveals a social paradox: Emphasizing individual differences inevitably leads to rapid social wealth accumulation and polarization and ignoring such disparities certainly causes slow social wealth accumulation.

In Extended Target Tracking, where estimating the shape is essential as kinematic, exploiting the dependencies between targets is often an excellent way to enhance performance. In a group of dependent targets, sampled features tend to have spatially and temporally correlations inside and between frames. Gaussian process regression has been used as a powerful Bayesian semi-supervised method to describe functions’ spatial and temporal correlation. This paper exploits and models the dependency between extended targets using Gaussian Process. We propose a novel recursive approach called Multi-Output Spatio-Temporal Gaussian Process Kalman Filter (MO-STGP-KF) to estimate and track multiple dependent extended targets that have possibly been degraded or covered with clutter. We used this method for detecting and tracking the group of connected lane markings called “lane-lines”. For detection and clustering, we propose a new Kernel-based Joint Probabilistic Data Association Coupled Filter (K-JPDACF) to cluster point features belonging to each lane-line. Compared to recently published model-based multi-lane tracking, semi-supervised, and fully supervised lane detection methods, our method shows 13 percent 34 percent and 20 percent improvement in accuracy, respectively.

When earthquakes occur, solar-powered unmanned aerial vehicles (SUAVs), deployed as communication relay points, can construct a signal relay network to assist the ground mobile communication vehicles in resuming communication. Considering the urgency of disaster relief, a practical, accurate, and robust modeling method for multiple SUAVs deployments is vital. For this concern, this article first formalizes the deployment problem of multiple solar-powered UAVs in communication recovery by extending Group MultiRole Assignment (GMRA) (UGRA). In the second step, the success in this assignment process depends on the choice of the agent evaluation method. The evaluation benchmark in UGRA is SUAV path planning in a complex environment with uncertain subpaths and accumulative attitude errors. In response to this issue, we propose two innovative algorithms: 1) dynamic curve path-planning algorithm (DCPPA) and 2) greedy curved straight path-planning algorithm (GCSPPA). Moreover, with the time requirement in mind, one sufficient condition and one necessary condition are established to help the DCPPA achieve fast convergence. With these two novel agent evaluation algorithms, UGRA can rapidly deploy multiple SUAVs to establish a collaborative relay network within an acceptable time. Finally, simulation experiments at different scales are carried out to demonstrate the accuracy and effectiveness of the proposed solution.

Portfolio creation and management are fundamental and important investment services. An investment manager is often responsible for dealing with many clients. Thus, s/he needs tools appropriate to creating, managing and matching portfolios with clients, according to market conditions. In planning, one financial product can be assigned to many, but different clients and one client may buy many, but different products. It is in fact a higherorder investment problem that involves many to many (M2M) assignment in the process. The manager must establish a mix of products, for various levels of return, that suit a client's risk tolerance. This paper solves the higherorder M2M assignment problem via the higher-order group multi-role assignment (HO-GMRA). Based on the concise formalization of Role-Based Collaboration (RBC) and its E-CARGO model, a practicable multi-object optimization approach is proposed, and its kernel is an x-ILP (extended Integer Linear Programming) planning method. Method verification is achieved by simulation experiments with respect to a real-world problem. The experimental results demonstrate the practicability of the proposed solutions.

Role-Based Collaboration (RBC) has emerged into an investigative methodology from a computational methodology with continuous research effort in the past decade. RBC uses roles as the primary underlying mechanism to facilitate collaboration activities. It consists of a set of concepts, principles, models, and algorithms. RBC imposes challenges and benefits not discovered in traditional methodologies and systems. RBC and the Environments - Classes, Agents, Roles, Groups, and Objects (E-CARGO) model have been investigated for over 18 years and have established a solid foundation for further research and investigation. Related research has brought and will bring exciting improvements to the development, evaluation, management, and execution of computer-based systems including services, clouds, productions, and administration systems. RBC and E-CARGO grow gradually into a strong fundamental methodology and model for exploring solutions to problems of complex systems including Collective Intelligence, Sensor Networking, Scheduling, Smart Cities, Internet of Things, Intelligent Transportation Systems, Cyber-Physical Systems, Social Networking, and Social Simulation Systems. In this keynote, we examine the requirement of research on collaboration systems and technologies, discuss RBC and its model E-CARGO; review the related research achievements on RBC and E-CARGO in the past years; discuss those problems that have not yet been solved satisfactorily; present the fundamental methods to conduct research related to RBC and E-CRAGO and discover related problems; and analyze their connections with other cuttingedge fields.

Oil resources are becoming increasingly scarce. Pure electric vehicles have huge advantages, in terms of energy efficiency and emission reduction. In cities, the locations of required charging stations and the number of required charging piles are determined according to the traffic flow on a road. Unreasonable allocation not only creates safety problems due to high electrical loads, but also increases the cost of the placements. Such allocations will involve the many-to-many (M2M) assignment in the process, which is necessary to establish an optimal model for distributing. Thus, this paper formalizes the charging pile siting problem (CPSP) via the group multirole assignment (GMRA) model, which is one of the most important methods to deal with the M2M problem. Based on GMRA, this paper proposes a role negotiation method by using a spectral clustering K-Means++ Algorithm based on location. The formalization of GMRA makes it easy to find a solution using the IBM ILOG CPLEX optimization package (CPLEX) via the Integer Programming (IP). All the proposed approaches are verified by simulation experiments, which have been proved to be efficient, feasible and practicable.

Team management is a highly complex decision-making procedure, which is normally accomplished by highly intelligent human personnel based on their knowledge, experience, communication skills, and wisdom. Task (Role) assignment is a crucial element of team management and can help decision-makers understand or predict future team performance.From the standpoint of the Environments – Classes, Agents, Roles, Groups, and Objects (E-CARGO) model as well as the Role-Based Collaboration (RBC) methodology, this paper uses four different approaches to conducting task assignment respectively: Group Role Assignment (GRA), GRA with Conflicting Agents on Roles (GRACAR), Best Agents for each Role (BAR), and BAR with Conflicting Agents (BARCA). After formalizations, we provide a comprehensive comparison among these methods of role assignments by simulations.The simulation results reveal interesting conclusions that help decision-makers understand the complexity of the assignment and choose a pertinent way when conducting team management.

Last-mile logistics is a crucial phase of online commodity trades. In last-mile logistics, one of the critical problems is to reasonably assign couriers to distribute the products in time in order to ensure the quality of service, especially for fresh produce. The last-mile assignment problem (LMAP) for fresh produce poses a challenge on traditional logistics since fresh produce is difficult to preserve. This article formalizes the LMAP for fresh produce via the group role assignment framework and proposes a role awareness method by using adaptive clustering in spatiotemporal crowdsourcing based on task granularity. The formalization of LMAP makes it easy to find a solution using the IBM ILOG CPLEX optimization package (CPLEX). The proposed method allows one to take the time and space factor into consideration, helps spatiotemporal crowdsourcing assign couriers for efficient delivering daily orders, and improves the quality of service in last-mile logistics. It is verified by simulation experiments. The experimental results demonstrate the practicability of the proposed solutions in this article.

Everybody knows that Mr. Donald Trump, the 45th President of the United States of America (USA), was against globalization. There are numerous arguments about this topic around the world among renowned politicians and economists. This article presents a new viewpoint from group multirole assignment (GMRA). In this article, we establish a model for simulating the assignment of grand capitals over the world with the help of the Environments—Classes, Agents, Roles, Groups, and Objects (E-CARGO) model and the GMRA model. To support the conclusions, we simulate the situations of globalization and nonglobalization, compare, and analyze the simulation results with a revised GMRA (RGMRA) model. This article contributes a new formalization of a new role assignment problem (RGMRA), a novel way to study globalization, and a clear and evident conclusion that globalization is not beneficial for the USA from the point of view of capital investment.

Currently, Edge computing (EC) paradigm is adopted to provision the low-latency resources for the massive real-time services in Internet of vehicles (IoV). To alleviate the QoE (Quality of Experience) degradation of the vehicular users due to the uncertainties (e.g., resource conflicts and communicating interruption), software-defined network (SDN) is involved in the EC-enabled IoV to manage the cooperative operation of distributed edge nodes (ENs). However, the increasing privacy leakage for the IoV service offloading causes the disclosure of the sensitive information, including driving location, personal information of the driver, etc. Moreover, the regulation of SDN is practically insufficient, as the general control is incompetent to maintain balanced operation with the premise of efficient service utility. In view of these challenges, a secure service offloading method, named SOME, is designed to promote IoV service utility and edge utility, meanwhile ensuring privacy security, in SDN-enabled EC. Specifically, an SDN-based framework for IoV service management is developed to address the inherent uncertainty of edge network by SDN controllers. Besides, the locality-sensitive-hash (LSH) is leveraged to realize utility- and privacy-aware service selection. Eventually, comparative experiments are implemented to verify the effectiveness of SOME.

As the number of cloud services (CSs) offering similar functionality is growing, more attention has been payed on the quality of service (QoS) of CSs. However, in a dynamic cloud environment, the explicit and inherent variation of QoS causes the single CS selection via collaborative filtering techniques (CSS-CFT) to be challenging. A variation-aware approach via collaborative QoS prediction is proposed to select an optimal CS according to users’ non-functional requirements. Based on time series QoS data, this approach utilizes a set of specific cloud models to quantify the variation characteristics of QoS from the four aspects including central tendency, variation range, frequency of variation and period. To exactly identify the neighboring users for a current user, this paper employs the double Mahalanobis distances to measure the similarity of QoS cloud models. The variation-aware CSS-CFT is formulated as a multi-criteria decision-making problem, and an improved TOPSIS method is exploited to solve it, by considering both the objective QoS variation and subjective user preferences during different time periods. The experiments based on a real-world dataset demonstrate that the proposed approach can enhance the accuracy of CSS-CFT in a high-variance environment without noticeable increase of selection time, in comparison to the existing approaches.

Recently, growing cloud services (CSs) have been leased by organizations for high-performance computation and massive data storage of data-intensive applications (DiAs). To improve the resource utilization of leased CSs, it has become a challenging task to optimize infrastructure as a service CS composition for DiAs (ICSCDs) from the user side. This paper proposes a resource utilization-aware collaborative optimization approach. Targeting the collaboration features of tasks in a DiA, the environments-classes, agents, roles, groups, and objects model is used to formalize the ICSCD problem from the perspective of role-based collaboration. Aiming at the dynamic characteristics of the cloud environment, an integrated method is presented to evaluate the qualification of CSs via the interval numbers with multiple parameters. Based on the exact qualification values, the ICSCD can be optimized for improving the resource utilization of the CSs. A solution using the IBM ILOG CPLEX optimization package is put forward to solve the problem. The experimental results demonstrate that the approach can provide high precision, performance, stability, resource utilization, and low usage cost for the resource utilization-aware ICSCD from the user side.

With the widespread application of service-oriented architecture (SOA), a flood of similarly functioning services have been deployed online. How to recommend services to users to meet their individual needs becomes the key issue in service recommendation. In recent years, methods based on collaborative filtering (CF) have been widely proposed for service recommendation. However, traditional CF typically exploits only low-dimensional and linear interactions between users and services and is challenged by the problem of data sparsity in the real world. To address these issues, inspired by deep learning, this article proposes a new deep CF model for service recommendation, named location-aware deep CF (LDCF). This model offers the following innovations: 1) the location features are mapped into high-dimensional dense embedding vectors; 2) the multilayer-perceptron (MLP) captures the high-dimensional and nonlinear characteristics; and 3) the similarity adaptive corrector (AC) is first embedded in the output layer to correct the predictive quality of service. Equipped with these, LDCF can not only learn the high-dimensional and nonlinear interactions between users and services but also significantly alleviate the data sparsity problem. Through substantial experiments conducted on a real-world Web service dataset, results indicate that LDCF's recommendation performance obviously outperforms nine state-of-the-art service recommendation methods.

In collaboration, one of the most important and most challenging problems is role assignment. This paper presents a unique approach to a "group role assignment" (GRA) problem that considers two previously established constraints within the same GRA problem. They are the busyness degree of different agents, and cooperation and conflict factors between agents. The aim of solving a "Group Role Assignment with Busyness degree and Cooperation and Conflict Factors" (GRABCF) problem is to assign agents to specific tasks in order to obtain the most efficient team possible. This paper's contributions include a formalization of the GRABCF problem, a simplified matrix to express cooperation and conflict, a practical solution for this problem, and simulations to prove the benefits of solving the GRABCF problem.

Exam scheduling is a complex problem. Traditional ways are using either graph-based heuristic algorithms or mixed linear programming (MLP) plus human intelligence. The heuristics could not always guarantee an optimal solution, and MLP, due to its generality, needs human schedulers to model for special requirements. Role-Based Collaboration (RBC) and its Environments - Classes, Agents, Roles, Groups, and Objects (E-CARGO) model and Group Role Assignment with Constraints (GRA+) are tools well suited to tackle such a problem. With RBC E-CARGO, and GRA+, human schedulers save a lot of effort to create symbols and the relationships among the components. This paper formalizes the problem of exam scheduling as an extended GRA with Conflicting Agents on Roles (GRACAR) problem, proposes practical solutions by using a Linear Programming (LP) solver, i.e., the IBM ILOG CPLEX Optimization Package (CPLEX). The proposed solution is verified by a real-world case study, and provides technical support for human schedulers in solving similar problems.

When an earthquake occurs, disaster relief is an urgent, complex and critical mission. High on the list is communication network recovery within the disaster area. Unmanned aerial vehicles (UAVs) are often used in this regard. Some of them are used as collective repeaters to provide the required network coverage. Their timely, efficient, and collaborative deployment to specific locations is a big challenge. To meet this challenge, this paper formalizes and solves the problem of UAV deployment for signal relays via group role assignment (GRA). The minimum spanning tree algorithm is used to model a rapidly deployed optimal relay network. It can help establish the minimum number of relay points necessary to ensure communication stability. In this scenario, UAVs (agents) adopt roles as communication relays. The task of distributing UAVs to relay points can be solved quickly via the assignment process of GRA, which can solve the x-ILP problem with the help of the PuLP package of Python. Results from thousands of experimental simulations indicate that our solutions are effective, robust and practical. The process can be used to establish an optimal, efficient, and collaborative relay network using UAVs. Their rapid deployment can be a significant contribution to earthquake disaster relief.

Task allocation is a critical phase of project management. Tree-type structures are frequently used constraints to obtain a pertinent task allocation. They can illustrate where one task may require numerous agents and when an agent can be assigned to different tasks (roles). The process of task allocation is made more complex when administrators need to satisfy sequential and fixed branch relationships between/among tasks (roles). This paper formalizes the tree-structured task allocation problem (TSTAP) with group multirole assignment (GMRA) and proves necessary conditions, the necessary and sufficient condition, as well as sufficient conditions, of TSTAP. The formalization makes it easy to find a solution with the IBM ILOG CPLEX optimization package (CPLEX). The necessary conditions improve the CPLEX solution by eliminating infeasible cases. The necessary and sufficient condition describes the solution space of TSTAP completely. Another exciting result is that the sufficient conditions can not only improve the CPLEX solution by describing a practical approximate solution space but also help decision-makers and human resource officers organize a team in order to successfully assign tasks. The proposed approach is verified by simulation experiments with respect to a real-world problem. The experimental results present the practicability of the proposed solutions in this paper. This paper was motivated by general cooperative projects whose tasks have tree-structured relationships. This can make the problem of successful multitask assignment extremely challenging. The traditional method of assignment such as the KM algorithm can no longer solve this problem. To solve the assignment problem with tree-structured relationships, an efficient many-to-many assignment with constraints is required. The proposed approach provides theoretical and technical foundations for efficient assignment of TSTA, which can not only provide a viable and effective assignment scheme for TSTA problems but also help human resource officers to formulate reasonable plans according to the relationships between/among tasks.

Agent categorization (AC) is a common, complex, and critical activity of collaboration and social systems. Specifying this problem through formalization and then determining an effective solution are an important and valuable task. This article formalizes the problem of AC by using the Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model. An innovative solution is proposed using the Group Role Assignment with Constraints (GRA+) algorithm and the Simulated Annealing (SA) algorithm. The proposed solution is verified by a real-world case study. Its practicability is confirmed through simulations using SA and the IBM ILOG CPLEX Optimization Platform (CPLEX). The contributions of this article include a novel formalization of the AC problem and an innovative and practical solution established by combining the GRA+and SA algorithms. The generalized form of this solution provides a solid foundation for decision-making over a broad range of applications.

Computational social simulation is a long-term, cutting-edge topic in the interdisciplinary field where information technology, computer science, social science, and sociology overlap. In this article, we establish the fundamental requirements for social simulation and demonstrate that the Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model for role-based collaboration (RBC) and the subsequent group role assignment (GRA) optimization model are highly qualified to meet these requirements. Based on E-CARGO and GRA, we propose a new approach to social simulation and conduct a case study to verify this approach. This case study involves a comparison between collectivism and individualism. The contribution of this work is a novel approach to social simulation using E-CARGO and GRA. This approach reveals the exciting results that explain social phenomena, e.g., collectivism is better than individualism if the team manager is perfect in the evaluation process, and individualism can beat collectivism without much difficulty if the team manager is not perfect.

Role assignment is an important and complex task in collaboration and management. Group role assignment (GRA) pursues the optimal team performance by assigning pertinent roles to agents in a team from the team's viewpoint. There are many factors that should be considered when conducting role assignment. Budgets are such a factor. This paper presents a challenging role assignment problem in collaboration, called GRA with budget constraints (GRABC). This paper contributes a thorough investigation of the GRABC problem including: 1) the first formalization of the proposed problem; 2) a set of practical solutions to different forms of this problem by using the IBM ILOG CPLEX optimization package (CPLEX); 3) a set of necessary conditions for these problems to possess feasible solutions to improve the CPLEX solutions; and 4) a synthesized process to deal with GRABC problems. Contribution 2) actually clarifies a list of requirements in solving the GRABC problem. Simulations, experiments and a case study are used to verify the practicability and efficiency of the proposed solutions.

It is an important topic to organize a team efficiently and keep it in a good state. In most cases, administrators try to avoid critical members. With this requirement, administrators prefer that their team members are able to be good at many things and expert in one (GMEO). However, that too many people are “good at many things” is definitely a waste. Role-based collaboration and its environments-classes, agents, roles, groups, and objects (E-CARGO) model are a good means to provide modeling and solutions to such a challenging problem. This paper formalizes the problem of GMEO into GMEO-1 (the fundamental form of GMEO) with the support of E-CARGO, clarifies two different forms of GMEO-1 and provides two highly practical solutions. The proposed solutions are verified by comparing with initial solutions using a linear programming solver, i.e., the IBM ILOG CPLEX Optimization Platform. The contributions of this paper are a thorough investigation of GMEO-1 that has no exact solutions even for a small group (e.g., ten people) and is normally managed by highly qualified administrators. The proposed solutions provide digital results with algorithms that form a solid foundation for decision making in dealing with similar issues.

Role negotiation is a pivotal step in the role-based collaboration (RBC) process. There are many factors to be considered when decision makers set up criteria to evaluate group performance and agents' abilities. In this paper, we investigate the applications of three traditional multicriteria decision making methods into role negotiation of RBC, i.e., simple additive weighting, multiplication exponent weighting, and weighted distance. The goal is to acquire ideal and satisfactory methods to set up the evaluation criteria for the role negotiation task. The main contributions of this paper include: 1) a method of generating the matrix of agent qualifications, with a discrete, usually, limited, and finite set of prespecified attributes and values; 2) the upper and lower thresholds of a role's ability requirements, which are critical parameters for a workable team and its performance; and 3) the potential influences of specialist agents and generalist agents on the group performance. The proposed methods are verified by simulation experiments. The simulation results show that the proposed methods are reasonable, feasible, and practical.

Redundant assignments are a common way to manage team performance. This requirement arises from real-world practices including team management, and highly-available systems. However, it is a complex problem due to limited resources. The lack of available modeling tools makes it even more difficult to provide an accurate solution. Role-Based Collaboration (RBC) and its Environments - Classes, Agents, Roles, Groups, and Objects (E-CARGO) model are tools well suited to tackle such a problem.This paper formalizes the problem of determining the most economical redundant assignment (MERA) with the E-CARGO model, clarifies it as a multi-objective optimization problem; proposes two solutions: one by using a Linear Programming (LP) solver, i.e., the IBM ILOG CPLEX optimization Platform (CPLEX); and one by using our previous work on Group Role Assignment (GRA), where the latter solution is much more efficient than the former one. This work contributes an investigation of the MERA problem to state of the art on the subject of assignment. The proposed solution provides technical support for decision making in solving similar problems.

The multi-valued evaluations of quality of service (QoS), the complicated constraints between cloud services (CSs) and the collaborative resource assignments add many difficulties to the problem of CS composition for data-intensive applications (DiA) in a hybrid cloud (CSCD-HC). Solving the CSCD-HC problem has become a challenging task due to the uncertain QoS, the diverse hardware configurations and the flexible pricing about CSs. This paper proposes a collaborative optimization approach for CSCD-HC. This approach models a DiA as a role-based collaboration (RBC) system and employs the environments-classes, agents, roles, groups, and objects (E-CARGO) model to formalize the CSCD-HC problem with complicated constraints. To deal with the multi-valued QoS evaluations, this paper exploits the cloud model theory to analyze the performance of CSs, and presents a new method utilizing the Mahalanobis distance to improve the similarity calculation of QoS cloud models. Based on it, the qualification of candidate CSs can be precisely measured for supporting CS composition. A solution via the IBM ILOG CPLEX optimization package is put forward to solve the CSCD-HC problem. The experimental results demonstrate that the proposed approach is effective and feasible for optimizing CSCD-HC.

Many to many (M2M) recommendation is one of the fundamental and important problems in commerce. With respect to this idea, profits, clients and products are inseparable. The traditional Top-N method cannot process the M2M recommendation problem. Therefore, this paper deals with the M2M recommendation as a many to many assignment problem via the group multi-role assignment (GMRA). Based on the concise formalization of Role-based collaboration (RBC) and its E-CARGO model, a successful approach using an (Extended Integer Linear Programming) x-ILP planning method and an improved greedy Top-N algorithm is proposed. These methods are verified by simulation experiments. Their results indicate the practicability of both solutions. As a comparison, the greedy Top-N method is faster than the x-ILP planning method via the PuLP linear planning package of Python. On the hand, the latter outperforms the former in recommendation accuracy.

With the growing cloud services (CSs) rented by an organization, it has become a challenging problem to optimize the CS composition for data-intensive applications (DiAs) from the user side, with consideration of improving the resource utilization of rented CSs. This paper proposes a resource utilization-aware approach to optimizing the CS composition for DiAs (CSCD). From the perspective of role-based collaboration, this approach utilizes the environments - classes, agents, roles, groups, and objects (E-CARGO) model to formalize the CSCD problem. The qualification of a CS for one task is assessed and the compatibility between new tasks and the running task is identified. A solution using IBM ILOG CPLEX package is put forward to optimize the CSCD problem. The experimental results demonstrate that the proposed approach is effective and feasible for optimizing the resource utilization-aware CSCD problem from the user side.

Cybersecurity, especially intrusion detection, is becoming increasingly critical in our daily life. The intrusion detection systems (IDS) have been widely used to prevent disclosure of personal information and detect potentially suspicious attacks. Although many machine learning algorithms have been broadly applied to enhance the performance of IDS, low detection rate and high false alarm rate are still two critical problems. A collaborative and robust intrusion detection model using a novel optimal weight strategy based on Genetic Algorithm (GA) for ensemble classifier is proposed in this paper. Since network data stream can be divided into three categories according to network protocols, detectors are applied in the network protocol separately. All of the detectors can work collaboratively and efficiently. In the proposed model, GA is used to optimize the weight of each base classifier of ensemble classifier. In order to improve features quality, Principal Component Analysis (PCA) is used for dimension reduction and attribute extraction. The NSL-KDD datasets is used to test the effectiveness of the collaborative intrusion detection model. Experimental results show that the proposed model has a higher accuracy and better generalized performance than others in this field.

In a team of collectivism, the team performance or the team interest is emphasized. Many individuals may find that the team interest is not always consistent with their own. Assigning positions to team members is one such scenario. An individual may be assigned to a position that is not preferred. Is there any policy for an individual to apply in order to get the preferred position? Role-Based Collaboration (RBC) is a methodology that advocates collectivism. The Environments - Classes, Agents, Roles, Groups, and Objects (E-CARGO) is a good tool to investigate this issue. The contributions of this work include: 1) a data analysis method for a person to pursue the preferred position (role) in a team based on the E-CARGO model and Group Role Assignment (GRA) algorithm; 2) a set of experiments for the proposed methods; and 3) the confirmation of a list of common sense principles with the support of experiments.

The major goal of organizing a team is to maximize the team's performance. However, team performance is a post-event measurement and difficult to manage before the teamwork starts. Therefore, team performance prediction is a significant and challenging problem. Role-Based Collaboration (RBC) and the Environments - Classes, Agents, Roles, Groups, and Objects (E-CARGO) model have been investigated for over a decade and have been developed into a promising tool to model activities and concepts of collaboration. This article proposes an innovative way to model and predict the performance of a team (group) based on the E-CARGO model and related algorithms. The proposed method is verified by a case study, which demonstrates the practicability of the proposed method.

Role assignment is a critical element in the role-based collaboration process. There are many factors to consider when decision makers undertake this task. Such factors include a decision maker's preferences and the team's performance. This paper proposes a series of methods, relative to these factors, to solve the group role assignment with balance problem through an association with the one clause at a time approach that is a well-accepted and logic-based association rule mining method. The proposed methods are verified by simulation experiments. The experimental results present the practicability of the proposed solutions. Using the proposed methods, decision makers need only to establish coarse-grain preferences. The fine-grain preferences can be mined. Furthermore, a balance is obtained between the fine-grain preferences and the team's performance.

Collaboration is complex. To solve a problem occurring in collaboration, using computers, we must first define and specify the problem. This paper presents a challenging problem in collaboration, called group role assignment with cooperation and conflict factors (GRACCFs). This problem's solution aims at creating a high-performance group by role assignment with consideration of cooperation and conflicts between agents. The contribution of this paper is the formalization of the proposed problem, a confirmation of the complexity of the problem, a practical solution that uses the IBM ILOG CPLEX optimization package (ILOG), a verification of the benefits of solving the GRACCF problem by simulations and a practical way of collecting the required factors to support decision makers in solving such a problem within a real-world scenario. Experiments are used to verify the efficiency of the proposed ILOG solution.

Cloud computing is growing rapidly due to its features of low cost, space saving, sharing etc., yet security issues are still a major concern. Multi-cloud computing technology was developed, in part, to address security concerns. One technique could use the Shamir's secret sharing method which is based on risk diversification. This work offers a solution strategy allowing users to allocate their resources to different cloud providers. In addition, we use trust values to measure a user's security when using cloud providers. Such values are used in contrast to the risk that user's resources may be leaked. The strategy is to find an appropriate plan whose trust value, from the user's perspective, is maximized by using a genetic algorithm. Experimental results indicate that the proposed solution strategy is effective and efficient.

Group role assignment (GRA) with flexible formation (called GRAFF) is a complex problem. The solution with GRA-Based on Exhaustive Search (GRA-ES) is too complex to be practical and the solution with Linear Programming-Based Algorithm (LPBA), implementing by Matlab and IBM ILOG CPLEX package, do not work well when the search space exceeds an extent. This paper proposes a solution to solve GRAFF based on the genetic algorithm (GRAFF-GA), specifies the processes of solving the GRAFF problem with GA, in which the fitness function adopts group role assignment (GRA), and the combination of crossover and mutation are based on natural genetic operators, verifies the GRAFF-GA method by simulations. The experimental results show that the method putting forward in this paper is an efficient approach.

Task allocations in collaboration are complex. This paper presents a challenging assignment problem, called Group Role Assignment with Agents' Busyness Degrees (GRAABD). The solution to this problem aims at creating a high-performance group by role assignment with consideration of the busyness degrees of agents. The contribution of this paper is the formalization of the proposed problem, a practical solution that uses the Group Role Assignment (GRA) algorithm, and a verification of the benefits of solving the GRAABD problem by simulations. Finally, a better solution to GRAABD, i.e., GRAABD-Syn is proposed, when the busyness' impacts on personal qualifications are considered.

Role assignment is a critical element in the role-based collaboration process. There are many different requirements to be considered when undertaking this task. This correspondence paper formalizes the group multirole assignment (GMRA) problem; proves the necessary and sufficient condition for the problem to have a feasible solution, provides an improved IBM ILOG CPLEX optimization package solution, and verifies the proposed solution with experiments. The contributions of this paper include: 1) the formalization of an important engineering problem, i.e., the GMRA problem; 2) a theoretical proof of the necessary and sufficient condition for GMRA to have a feasible solution; and 3) an improved ILOG solution to such a problem.

Adaptive Collaboration (AC) has been proven and verified to be a promising methodology to conduct collaboration. AC follows an iterative and dynamic process that includes phases of role negotiation, agent evaluation, role assignment, role playing, and role transfer. However, AC is complex, and the benefits of AC are not easily observed. In particular, the benefits must be obtained through complicated computations and should be further presented appropriately, e.g., visualization. This paper proposes an approach to visualizing the benefits of AC in group performance. Such visualization can illustrate how dynamic role assignment performs better than static role assignment. Our research is verified through implementations, a case study, and a usability study.

In the real-world, there are many criteria to evaluate agents on roles, which are an important factor affecting the result of the group role assignment (GRA). To obtain a satisfied assignment, we need to select an appropriate evaluation method. This paper points out the key issues of the related concepts, formalizes the problem of GRA based on multiple criteria (GRA-MC), investigates three algorithms of multi-criteria decision making, i.e., simple additive weighting (SAW), multiplication exponent weighting (MEW) and weighted distance (WD), briefs the procedure of GRA-MC, verifies the GRA-MA methods by simulations and a case study, and finally discusses the result of GRA-MC.

Adaptive collaboration (AC) is essential for group performance optimization in collaborative systems. This paper begins by introducing AC within the context of solving a real-world problem. Next, AC problems are formalized based on the environment-class, agent, role, group, and object (E-CARGO) model. Three algorithms are proposed for solving AC problems. They are based on three different scenarios: 1) the current group state (GS); 2) the GS after a specific period; and 3) the GS throughout the collaboration. More complex AC problems and their solutions are then investigated. Derived from the above-mentioned algorithms, two additional algorithms are presented. They consider reassignment costs. Experiments are developed to analyze the performance of each proposed algorithm. Results indicate that the proposed algorithms perform better than static collaboration where there are no reassignment costs. If reassignment costs exist, we also provide a way of determining whether to adopt an AC approach. This paper provides insights into the AC process and its effectiveness in various scenarios.

In order to select effective policies for emergency management in a timely manner, this paper proposes an agile and lightweight social computing approach to facilitating policy selection, evaluation, and adjustment relative to emergency management in both quantitative and qualitative ways. The approach consists of three components represented as PZE: 1) (P) emergency management policy selecting; 2) (Z) modeling artificial societies with the zombie-city model (a general and formal artificial society model); and 3) (E) policy evaluation. The formal specification of the zombie-city model and rigorous expressions of scenarios enable rigorous description and formal reasoning of an artificial society. A feedback loop of this approach supports the iterative adjustment of emergency management policies and the creation of more effective policies. This approach is verified by applying it to a case of an infectious disease transmission with quantitative evaluations, qualitative reasoning and analysis, and iterative adjustments. Results indicate effective emergency management policies can be established with the approach in an iterative way. In contrast with existing research, our proposed approach offers the benefits of being simple, general, rapidly adaptive to changes, and low cost.

Role assignment is a critical element in the role-based collaboration process. There are many constraints to be considered when undertaking this task. This paper formalizes the group role assignment problem when faced with the constraint of conflicting agents, verifies the benefits of solving the problem, proves that such a problem is a subproblem of the extended integer linear programming (x-ILP) problem, proposes a practical approach to the solution, and assures performance based on the results of experiments. The contributions of this paper include: 1) formalization of the proposed problem; 2) verification of the benefit achieved by avoiding conflicts in role assignment through simulation; 3) theoretical proof that conflict avoidance is a subproblem of the x-ILP problem that is nonpolynomial-complete; and 4) a practical solution based on the IBM ILOG CPLEX optimization package (ILOG) and verification of the scale of problems that can be solved with ILOG. The proposed approach is validated by simulation experiments. Its efficiency is verified by comparison with the previous exhaustive search-based approach.

To maintain the performance of a group is one objective in collaboration. Adaptive Collaboration (AC) is targeting this aim by conducting multiple role re-assignments during the collaboration process. This paper begins by clarifying AC. Then, the AC problem is formalized based on the Environment-Class, Agent, Role, Group, and Object (E-CARGO) model. Based on the general AC model, we consider a typical scenario where individual agents change their performance in different ways. To conduct AC with such a scenario, we propose an approach. Simulations are conducted to verify the benefits of the AC approach in this scenario. Results indicate that the proposed AC approach performs better than static collaboration in the assumed scenario with consideration of costs.

In the process of Research and Development (R&D), the staff in Information Technology (IT) companies forms a collaboration system. Task assignment is very important in the process of R&D. Firstly, this paper formalizes the staff and task assignment problems (STAPs) based on the Environment, Class, Agent, Role, Group and Object (E-CARGO) model. Next, we propose an algorithm for the STAPs. Then, we verify the proposed algorithm with experiments. The experimental result shows that the proposed algorithm is effective.

Natural disasters can lead to severe losses in human life and property. Because many factors combine in a disaster, such events are difficult to forecast accurately. A cooperative multi-classifier method is proposed in this paper to mine local area meteorological data. The proposed method is verified by the implementation of both base and integration classifiers. Experimental results indicate that our proposed method has higher classification accuracy and faster grouping ability compared with conventional classifiers.

Role-Based Collaboration (RBC) is a computational thinking methodology where roles provide an underlying mechanism to facilitate abstraction, classification, separation of concerns, dynamics, and interactions. From a meta theoretical perspective, the specification of groups, roles and agents is a critical element of RBC. In consideration of the relationships and hierarchies faced by groups, roles and agents, we propose a minimal role playing logic system (MRPL) through substructural logic, which is polynomial in complexity, in support of RBC. From MRPL and RPLs extending from it, there are three levels of application, i.e., the global level governing how people organize agents to form a group; the concatenative level for role assignment with respect to logic and algebra, and the operational level governing properties, relations and structures that should appear in collaborative system design. From MRPL to RPLs, one can extend it to suit other appropriate applications.

Group role assignment with a flexible formation (GRAFF) is essential for group performance optimization in collaborative systems. In this paper, problems of GRAFF are formalized based on the Environment-Class, Agent, Role, Group, and Object (E-CARGO) model. Then, based on group role assignment (GRA) and linear programming (LP), two algorithms are proposed. Experiments are developed to analyze the performance of each proposed algorithm. Results indicate that the proposed algorithms are effective and the linear programming-based algorithm implemented with the IBM ILOG CPLEX package is the most efficient way to solve problems of GRAFF.

Choosing whom to tweet information to promote information spread on social media is an interesting and significant topic for both academic and industrial areas. Effective policies to choose whom to tweet information on social media should make more users to be aware of the information and to be willing to retweet it. Aiming at investigating how information spreads on social media, this paper proposes an interest-based dissemination model to depict the information spread process. The interest is introduced as the foundation of users' rationality to follow other users and retweet information. Meanwhile, this paper adopts artificial society and a lightweight social computing method to evaluate and analyze the effectiveness of policies, in which users on social media are modelled as agents with various social relationships and interests. Based on PZE approach, several policies for information promotion on social media have been made, and simulations are undertaken quantitatively to evaluate their effectiveness in various scenarios. The experimental results reveal that the effectiveness of a policy is tightly related to two kinds of users' rational behaviors: retweeting information and following other users.

We firstly analyze the method for four attack types, including Probing, DoS (Denial of Service), R2L (Remote to Local) and U2R (User to Root). Based on resource addresses and destination addresses of the network packages, attacks can be divided into four cases, which are respectively one host-one host, one host-many hosts, many hosts-one host and many hosts-many hosts. Specifically, the granular computing method is applied in intrusion detection. A cooperative intrusion detection model is proposed based on granular computing. The construction for an intrusion detection agent is presented.

Group role assignment with constraints (GRAC) is an important task in Role-Based Collaboration (RBC). The complexity of group role assignment becomes very high as constraints are introduced. This paper describes the group role assignment problem with conflicting agents; analyzes the complexity of the problem, proposes a solution based on the IBM ILOG CPLEX optimization package; conducts comparisons with our early work by experiments; analyzes the solutions' performances, and indicates the improvement of this proposed solution. The contribution of this paper includes: clarifying the complexity of the discussed problem; proposes a better solution, and verifies its efficiency and practicability.

Adaptive Collaboration (AC) aims at obtaining the overall optimal group performance. To obtain a group performance, we need to evaluate individual performance of each member in a group. This paper points out the key issues in evaluating the individual performance, formally specifies the related concepts, proposes an evaluation method based on role specification, and verify the proposed method by a case study.

SUMMARY Role‐based collaboration is an emerging methodology to facilitate an organizational structure, provide orderly system behavior, and consolidate system security for both human and non‐human entities, like agents, that collaborate and coordinate their activities with or within systems. Interaction management must, however, be able to handle run‐time and dynamic scenarios. Hence, every role‐based collaboration system must provide a good level of dynamism, that is, provide an agent with the capability to assume, use, and release a role depending on run‐time conditions. Dynamism, however, does not suffice in adaptative scenarios: being able to use a role dynamically is important, but in order to enhance interagent communications, the capability to perceive a played role is important too. Role perceivability is the capability of an agent to autonomously recognize the role played by another entity without the need to ask a yellow‐page directory. Whereas dynamism has been achieved with different techniques and often through language support, role perceivability is more difficult to achieve and to some extent even more important because it can boost sociality among entities and agents. In object‐oriented programming languages, such as JAVA, role perceivability could be achieved with appropriate changes to the agent/entity class structure, but this requires compile time constraints that are, in their nature, not dynamic. This paper proposes an approach to remedy the above problems: maintaining an appropriate level of dynamism. The work presented here allows a JAVA agent to make its role perceivable to other entities as if it is applied at compile time. Copyright © 2011 John Wiley & Sons, Ltd.

Outpatient scheduling is considered as a complex problem. Efficient solutions to this problem are required by many health care facilities. This paper proposes an efficient approach to outpatient scheduling by specifying a bidding method and converting it to a group role assignment problem. The proposed approach is validated by conducting simulations and experiments with randomly generated patient requests for available time slots. The major contribution of this paper is an efficient outpatient scheduling approach making automatic outpatient scheduling practical. The exciting result is due to the consideration of outpatient scheduling as a collaborative activity and the creation of a qualification matrix in order to apply the group role assignment algorithm.

Many-to-Many (M-M) role transfers are generalized problems that are encountered in collaboration. Exhaustive-search-based algorithms are too computationally intensive. This paper introduces the Kuhn-Munkres (or Hungarian) algorithm for the general assignment problems (GAPs) and proposes a new efficient algorithm to solve the M-M role transfer problems by converting them to the GAPs. The experiments and results validate the proposed algorithms.

Role assignment is a critical task in role-based collaboration. It has three steps, i.e., agent evaluation, group role assignment, and role transfer, where group role assignment is a time-consuming process. This paper clarifies the group role assignment problem (GRAP), describes a general assignment problem (GAP), converts a GRAP to a GAP, proposes an efficient algorithm based on the Kuhn-Munkres (K-M) algorithm, conducts numerical experiments, and analyzes the solutions' performances. The results show that the proposed algorithm significantly improves the algorithm based on exhaustive search. The major contributions of this paper include formally defining the GRAPs, giving a general efficient solution for them, and expanding the application scope of the K-M algorithm. This paper offers an efficient enough solution based on the K-M algorithm that outperforms significantly the exhaustive search approach.

Role assignment is an important task in Role-Based Collaboration (RBC) and Adaptive Collaboration. There are many variations in the process of role assignment. An introduction of any new requirement leads to a significant change in the process and the complexity of the algorithm of role assignment. This paper formally identifies a group of problems in role assignment when multiple agents are involved; proposes initial solutions; conducts simulations and experiments to reveal the complexity of collective group role assignment, and indicates future work activities. The contributions of this paper include formal provision of taxonomy for collective group role assignment and indication of the complexities arising from of this type of problem through simulations and experiments.

Intelligent adaptive interfaces (IAIs) are emerging technologies that promise opportunities for enhancing performance in complex sociotechnical environments, such as multiple uninhabited aerial vehicle (UAV) control. However, a lack of established design guidelines for such advanced interfaces makes many designs costly and ineffective. In this paper, a generic conceptual framework for developing IAIs is proposed to guide interface design. The framework integrates a user-centered design approach with the concept of proactive use of adaptive intelligent agents (AIAs), aiming at maximizing overall system performance. Based on existing design approaches, identified challenges, and IAI design needs, the framework uses a multiple-agent hierarchical structure to allocate tasks between operators and agents for optimizing operator-agent interaction. These AIAs provide interface aids as a means of reducing operator workload, and increasing situation awareness and operational effectiveness. The framework and associated IAI models provide guidance to design a knowledge-based system, such as a UAV control station interface.

In the modern world, adaptability is a key performance index for a group to be competitive. The use of well-designed computer tools by its members can help increase its adaptability. The purpose of Adaptive Collaboration (AC) is to obtain the optimal group performance, e.g., the maximum sum of the individual performances of group members in their positions for a concerned period of time. AC is a natural extension of Role-Based Collaboration (RBC). Our early model E-CARGO (Environments - Classes, Agents, Roles, Groups, and Objects) of RBC can become a fundamental model for AC. This paper points out the key issues facing AC researchers, analyzes the key factors related with adaptability, clarifies the AC process, and outlines some preliminary yet promising solutions to some issues.

Role assignment is a critical task in Role-Based Collaboration (RBC). It can be divided into three steps, i.e., agent evaluation, group role assignment, and role transfer, where group role assignment is a time-consuming process. This paper clarifies the problem of group role assignment; describes the general assignment problem; adapts the group role assignment problem to the general assignment problem; proposes an algorithm for group role assignment based on the Munkres algorithm; conducts an experiment by randomly creating agent qualifications, analyzes the solutions' performances, and points out the future work. The results show that the improved algorithm significantly improves the algorithm based on exhaustive search.

Many-to-many (M–M) role transfers are generalized problems that occur normally in collaboration, management, and the operation of organizations. This paper extends our previous work on role transfer. It is the first attempt to formalize the M–M role-transfer problems with real-world management examples, analyze the different cases of M–M problems, design algorithms for all the cases, implement all the algorithms to provide solutions, and discuss the future work. The significant contribution of this paper is to demonstrate that role-transfer problems have definite solutions. The implementations and their results validate the proposed algorithms.

This paper discusses that Role-Based Management (RBM) is applied in a Web-based teaching system. A user may have multiple roles. A role may be given to multiple users. Different roles have different privileges. From the implementation, it is found that roles are good mechanisms in designing a learning system.

When a crisis occurs, decision makers experience high tension and must make a decision in a short time. An automated tool with accurate analysis capability would help them make correct decisions. This paper presents a visualized tool to help a decision maker understand the structure of a group based on roles and agents (or people). The significant contribution of this tool is that it provides an exact solution to check if a group is workable, and if an agent (or a person) is critical for a group. It also suggests a role transfer scheme implementing time sharing mechanisms for when there are an insufficient number of agents (people) to complete a task simultaneously. Because role transfer is a fundamental problem in management, task assignment, and training, this tool can be used in many different ways, such as, training and management. It is also a direct assistance tool for crisis responses.

Role-based approaches are emerging technologies in information system design and implementation. Roles have been acknowledged and applied in many fields for many years. Considering their increasing importance and applications in the development of various information systems, this paper intends to: 1) survey the literature relevant to role mechanisms and role-based systems in different fields and point out their motivations and contributions; 2) classify roles in such different contexts as modeling, designing, management, and collaboration; 3) identify the commonalities and differences among roles in different fields; and 4) point out the challenges, benefits, and future research areas of role-based systems.

Role transfer is a usual activity in an organization, particularly in a crisis situation. Role assignment and transfer regulations are important to accomplish it. This paper discusses the general role transfer problem; proposes a role specification mechanism; builds a set of terminologies for role transfer based on a revised environment-class, agent, role, group, and object model; and presents algorithms to validate role transfer while maintaining group viability. The contributions include formulating the problem of role transfer in a generalized form, developing a set of algorithms, and presenting a solution when group members are insufficient.

Role-based collaboration (RBC) is proposed to support people’s collaboration with more usable human-computer interfaces. To approach the objectives of RBC, new practical tools are required. Designing, implementing and using the new tools are the major methods to accomplish the tasks of research on RBC. This paper presents a tool for role-based chatting that is a typical instance of role-based collaboration. The scenario of role-based chatting, and the tool’s architecture and implementation are described. The chatting tool reflects all the principles outlined by role-based collaboration. This tool shows that role-based collaboration is practical, and feasible. It also shows the possibility of building more complex rolebased systems.

In recent years, kNN algorithm is paid attention by many researchers and is proved one of the best text categorization algorithms. Text categorization is according to training set, which is assigned class label to decide a new document, which is not assigned class label belongs to some kind of document. But for a classifier, text preprocessing is the bottleneck of categorization. In the original feature space, there are always thousands upon thousands words. The dimension of feature space is very high. So in this paper, we adopt a new feature weight method---- improved Gini index to reduce the dimension of feature space and improve the categorization precision. In addition, we discuss the improvement of decision rule and dimension selection. We design an adaptive fuzzy kNN text classifier. Here the adaptive indicate the adaptive of dimension selection. The experiment results show that our algorithm is effective and feasible.

Robot team work is a typical collaborative activity. How to organize robot teams and design robots are important and complex tasks. This paper explores the properties of agents and agent systems, introduces and revises the role-based collaboration model E-CARGO, demonstrates the essential design concerns of roles, agents, agent collaboration and agent teams, and proposes that roles can be used to form a fundamental structure, i.e., role nets, to design robot agent teams. Finally, it points out the research topics of role-based design for robot agent systems.

Computer-supported cooperative work (CSCW) systems are computer-based tools that support the collaborative activities of human users. They should not only support virtual face-to-face collaborative environments but also improve face-to-face collaboration by providing more mechanisms to overcome the drawbacks of usual face-to-face collaboration. Introducing roles into CSCW systems is important in achieving this. This paper's contributions include establishing the requirements for a role-based collaboration, presenting the concept, requirements, and principles of role-based collaboration, proposing a model E-CARGO for role-based collaboration, and describing the kernel mechanisms and their implementation to facilitate the development of role-based collaborative systems for industrial applications.

Software development tools are very important in software engineering. Although roles have been acknowledged and applied for many years in several areas related to software engineering, there is a lack of research on software development tools based on roles. Most significantly, there is no complete and consistent consideration of roles in all the phases of software development. Considering the increasing importance and applications of roles in software development, this paper intends to discuss the importance of roles in software engineering and that of role-based software development; review the literature relevant to role mechanisms in software engineering; propose and describe a role-based software process; and implement a prototype tool for developing complex software systems with the help of role mechanisms

Teamwork requires every member to contribute to the team. Encouraging people to contribute in collaboration is exciting and challenging. The model E-CARGO could support collaboration with human users, agents, roles and groups. By assigning roles and transferring roles, we can organize and encourage human users and agents to contribute in collaboration and make the teamwork more profitable. This paper analyses the behaviors of people in collaboration, proposes a methodology with roles to improve collaboration by assigning human users more roles, discusses the basic methods to evaluate and promote human users with roles and relevant properties and describes the implementation of the kernel mechanisms.

Conflict resolution is one of the most important problems that must be solved in building a collaborative system. In many CSCW (computer-supported-cooperative work) applications or systems, roles were granted in the design and application of these systems. Consequently, we may infer that "without roles, there would be no collaboration". Based on the success of RBAC, we found that role-based methods are very useful in building collaborative systems. However, there is little research on conflict resolution based on roles. This paper, briefly introduces an object model for collaborative systems OMCS and a multimedia co-authoring system MCAS, and mainly discusses the role management and a role-based conflict avoidance and resolution method in the system MCAS. It emphasizes the importance of roles in conflict avoidance and resolution. In the last section, it summarizes the paper and draws a conclusion is drawn that a role-based method can help greatly to resolve conflicts in collaborative systems.