Partitioning a pool of capability-heterogeneous human and machine agents into effective teams for multiple concurrent tasks is a fundamental challenge in hybrid human–machine collaboration. We formalize this problem as aMulti-Task Additively Separable Hedonic Game(MT-ASHG), in which every agent is a self-interested player whose utility combines (i) a task-specific proficiency score measuring how well the agent’s skill vector aligns with task requirements, and (ii) an inter-agent compatibility score capturing synergistic or conflicting partnerships. Building on this formulation, we design an Iterative Best Response (IBR) algorithm that lets agents autonomously migrate between task groups to improve their individual payoffs. We prove that the IBR dynamics converge in a finite number of steps to an individually stable partition, in which no agent can unilaterally improve its utility by switching teams, and analyze the computational complexity of the convergence process. To bridge theory and practice, we further introduce an empirical profiling method that extracts capability and compatibility vectors from historical agent interactions, addressing the cold-start problem in newly formed human–machine teams. Extensive experiments on synthetic benchmarks and the Overcooked-AI cooperative environment demonstrate that MT-ASHG consistently outperforms centralized assignment baselines and existing coalition-formation methods in terms of global task completion rate, fairness, and scalability.

In recent years, industrial chain cooperation has evolved into multiplex network structures where product agents are linked through diverse types of interdependencies. While such architectures enhance coordination flexibility, resource-sharing efficiency, and system-level resilience, they also introduce complex hybrid dynamics. These dynamics emerge from fluctuating task demands, continuous changes in network topology as agents join or leave, and variations in production capacities across agents. Their interactions generate cascading cross-layer effects that disrupt load balance and challenge conventional scheduling and resource management strategies. This work addresses the resulting complexity by proposing the hierarchical grouped task migration (HGTM) algorithm, which migrates tasks in groups rather than individually. Leveraging its hierarchical design, HGTM enables effective multilevel load balancing throughout the multiplex structure while keeping computational overhead low. Comprehensive theoretical analysis and experiments show that HGTM enhances task completion rates, improves execution utility, and reduces completion costs. The approach exhibits strong robustness and adaptability, particularly under increasingly dynamic and highly coupled operating conditions.

Industrial software systems underpin complex industrial platforms by integrating cross-disciplinary expertize and sophisticated workflows. Their inherent complexity generates substantial cognitive demands, necessitating contextual, adaptive, and personalized knowledge support aligned with dynamic tasks and evolving expertize. Conventional recommendation approaches struggle to address heterogeneous knowledge sources, dynamic user needs with intent drift, and the domain-specific semantics required in industrial software. To overcome these challenges, we propose a context-aware dialog-based knowledge recommendation (CADKR) model, powered by large language models (LLMs). CADKR fuses dynamic interaction context with domain knowledge through a novel recommendation network (RecNet), enabling robust generalization to unseen scenarios and adaptability to preference drift. For practical deployment, lightweight optimization strategies compress model size by up to 98% without compromising accuracy. A case study in a large chemical industrial park demonstrates the effectiveness of CADKR in enhancing industrial knowledge support, while application deployments in leading new energy vehicle enterprises and the world’s largest circular resource power plant have yielded an independent verification report, providing strong evidence of the proposed method’s practical value.11The report is available athttps://anonymous.4open.science/r/verification-report. Code and data are available athttps://anonymous.4open.science/r/CADKR-TCSS. To meet double-blind requirements, organization and personnel details are anonymized and will be disclosed after acceptance.

In industrial production processes, disruptions within the industrial chain can severely affect the collaborative capabilities of production agents. A notable example occurred during the COVID-19 pandemic, when many agents faced interruption risks and were unable to participate in coordinated production. Ensuring continuity under such conditions requires migrating tasks from disrupted agents to viable alternatives. Designing effective task migration strategies, however, must account for the emergent multiplex nature of modern industrial chains. In these multiplex networked industrial chains, disruption risk in one layer can propagate to others, generating cascading failures across the system. This introduces two key challenges: (1) disruption risk creates mismatches not only between product agents and tasks but also across network layers, enlarging the problem dimensionality; and (2) simultaneous disruptions across multiple agents and layers increase the volume of tasks needing migration, greatly expanding the solution space. To address these challenges, we introduce the notion of a multiplex potential field, which captures cross-layer interdependencies and system-level dynamics in multiplex industrial chains. Building on this concept, we develop a hierarchical contextual task migration algorithm that exploits the multiplex potential field to guide both inter-layer and intra-layer task reallocations. Extensive experiments show that our approach consistently achieves superior utility, markedly improves task completion ratios, and reduces execution costs compared to benchmark algorithms. Furthermore, it attains solution quality comparable to that of the optimal CPLEX solver while requiring substantially less computation time. Finally, a case study on the FAO international food trade network demonstrates that the proposed framework is not only theoretically robust but also practically effective when deployed on large-scale real-world multiplex systems.

In the heterogeneous and dynamically evolving Industrial Internet, industrial agents are required to possess cross-domain adaptability and self-learning capabilities to facilitate task generalization and scalable deployment across diverse operational contexts. However, existing domain adaptation approaches predominantly rely on static feature alignment or domain-invariant assumptions, lacking a systematic consideration of working condition variability and the interplay between self-learning and adaptation. This oversight hampers their effectiveness in real-world industrial scenarios, where agents must operate under complex conditions with limited target domain knowledge. Consequently, these methods often suffer from knowledge shift and insufficient policy generalization. To address these limitations, this article introduces the instance weighting-based domain-adaptive optimization (IW-DAO) framework. IW-DAO combines an instance weighting-based knowledge alignment mechanism with a Bayesian optimization strategy, forming a dynamic self-learning loop tailored for cross-domain adaptation. Specifically, the framework constructs an adaptive knowledge representation in a high-dimensional invariant feature space and formulates a cross-domain performance evaluation estimator to guide the unsupervised learning of knowledge transfer and adaptive optimization via Bayesian iterative search. Extensive experiments on industrial asset management tasks as well as a real-world industrial flow process dataset with various operating conditions demonstrate the effectiveness of IW-DAO. The proposed framework enables industrial agents to evolve autonomously and be deployed efficiently across diverse domains. IW-DAO consistently outperforms baseline and expert-tuned methods, demonstrating strong generalization and adaptability in both industrial asset management and complex flow process scenarios.

From the perspective of service computation, the current service-oriented architecture of industrial chain networks faces issues such as the lack of multiple computing services and insufficient detection capabilities, especially in the context of insider trading detection services for multiplex networked industrial chains. Existing service computation methods are often limited to single-market or simplified network structures, making it difficult to fully capture the dynamic changes and cross-chain propagation characteristics of insider trading within complex multi-layered industrial chain networks. Therefore, developing an efficient and accurate insider trading detection service computation method in the environment of multiplex networked industrial chains remains a critical research challenge. To address this, this paper proposes an Insider Trading Detection Service Computation Method for Multiplex Networked Industrial Chains based on Hybrid Temporal Granularity Scaling (ITDSC-MNICHTGS). This method combines insider trading feature modeling in multiplex networks, an adaptive time granularity adjustment mechanism, and unsupervised learning techniques to accurately identify insider trading behaviors in multiplex networked industrial chains without relying on data labeling. Experimental results show that, compared to traditional detection methods, the proposed method outperforms in metrics such as precision, recall, and$\mathbf{F 1}$-score, effectively improving the reliability and applicability of insider trading detection.

The emergence of diversified computing paradigms and the proliferation of interconnected devices have transformed modern service computing systems into multiplex service networks. In these networks, services are provisioned across multiple interconnected layers, such as infrastructure layer, platform layer, and application layer. A distinctive characteristic of these systems is the presence of hybrid dynamics, characterized by the complex interplay of service request dynamics (random service request arrivals), service network dynamics (topology changes due to agent joins and leaves), and service resource dynamics (fluctuating service provisioning capabilities). These interrelated hybrid dynamics frequently propagate bidirectionally across system layers, creating intricate emergent behaviors and potentially triggering cascading load imbalances that can significantly degrade system performance and reliability. To comprehensively address these multidimensional challenges, we propose a novel and adaptive group-based task migration methodology. By systematically migrating tasks in cohesive groups rather than as isolated individual entities, this innovative approach effectively buffers the destabilizing impact of hybrid dynamics while simultaneously reducing computational and communication overhead associated with frequent decision-making processes. Through rigorous theoretical analysis and extensive experiments, our proposed method shows remarkable advantages in service computing scenarios with hybrid dynamics. The results validate the effectiveness of our group-based migration paradigm in addressing the challenging requirements of modern multiplex service computing systems.

The increasing complexity of industrial information service systems presents significant challenges for anomaly detection, particularly in ensuring service reliability across multilayered service networks. This paper proposes TS-GNN, a novel Temporal-Spatial Graph Neural Network framework that effectively integrates temporal pattern recognition with spatial dependency modeling for anomaly detection in industrial service environments. The framework employs a multi-scale temporal feature extraction mechanism that combines frequency-domain transformation with hierarchical decomposition to capture temporal patterns at different granularities. Subsequently, a graph neural network with attention-based message passing models spatial correlations and anomaly propagation patterns among service nodes. Comprehensive experiments on three benchmark datasets from service computing domains demonstrate that TSGNN achieves superior performance, with F1-scores of 94.74% on SWaT, 85.14% on SMD, and 96.36% on PSM datasets. Compared to the best baseline methods, TS-GNN shows consistent improvements with an average$\mathbf{F 1}$-score enhancement of$\mathbf{0. 7 9 \%}$points, providing an effective solution for enhancing the reliability and robustness of service computing systems.

To better identify user service needs and preferences, proactive conversational interactions are essential, a concept encapsulated by Conversational Recommender Systems (CRS) in service and recommendation domains. A key challenge faced by CRS lies in accurately capturing user preferences from dialogue contexts, particularly in non-stationary environments where traditional methods are hindered by cold-start problems and shifting service demands. Motivated by the strong generalization abilities of In-Context Learning (ICL) in dynamic and unfamiliar scenarios, this paper proposes ICD4CR, a causal decision-making framework grounded in in-context decision-making principles. Leveraging large Language Models (LMs), ICD4CR adopts a data-driven pretraining paradigm, enabling it to infer optimal recommendation strategies from historical dialogue trajectories in analogous service contexts, circumventing the need for explicit user modeling. We introduce a recommendation network that integrates seamlessly with the foundational LM, allowing ICD4CR to function as a fully end-to-end recommendation system. To enhance efficiency and adaptability, adapter-based techniques are employed for knowledge transfer and fine-tuning.

With the rapid development and deep integration of artificial intelligence and automation technologies, autonomous unmanned swarms dynamically organize into multiplex network structures based on diverse task requirements in adversarial environments. Frequent task variations lead to load imbalances among agents and between network layers, significantly increasing the risk of enemy detection and destruction. Existing approaches typically simplify multiplex networks into single-layer structures for task scheduling, failing to address these load imbalance issues. Moreover, the coupling between task dynamics and network multiplexity dramatically increases the complexity of designing task migration strategies, and it is proven NP-hard to achieve such load balancing. To address these challenges, this paper proposes a risk-aware task migration method that achieves dynamic load balancing by matching task requirements with both intra-layer agent capabilities and inter-layer swarm capabilities. Simulation results demonstrate that our approach significantly outperforms benchmark algorithms in task completion cost, task completion proportion, and system robustness. In particular, the algorithm achieves solutions statistically indistinguishable from the optimal solutions computed by the CPLEX solver, while exhibiting significantly reduced computational overhead.

Determining the optimal number of clusters in cluster analysis without prior knowledge remains a critical and challenging task. Existing methods often depend on calculating clustering validity indices (CVIs), which increases complexity and may reduce efficiency. Furthermore, different CVIs frequently suggest varying optimal cluster numbers, complicating the selection process. To address these challenges, we propose a novel clustering algorithm, self-regulating possibilistic C-means (PCM) with high-density points (SR-PCM-HDP), which simplifies cluster number determination while improving clustering efficiency. First, the density-based knowledge extraction (DBKE) method is introduced to estimate an appropriate initial cluster number and identify high-density points. DBKE enhances the density peak clustering (DPC) algorithm by removing the need for a predefined density radius. Second, SR-PCM-HDP refines the clustering process by incorporating a parameter to balance the interactions between high-density points and cluster centers, reducing sensitivity to initial configurations and accelerating convergence. Third, the parameter adjustment mechanism in classical PCM is redefined to enable adaptive updates during SR-PCM-HDP iterations. This mechanism facilitates the gradual elimination of obsolete clusters and iterative cluster formation. The theoretical foundations of the SR-PCM-HDP cluster elimination mechanism are rigorously established. Experimental results validate the accuracy and effectiveness of SR-PCM-HDP in determining cluster numbers and ensuring clustering validity, particularly for datasets with overlapping or imbalanced distributions. Comparisons are conducted against 13 state-of-the-art algorithms, including fuzzy clustering, possibilistic clustering, and CVI-based cluster determination methods.

Multi-view attributed graphs (MVAGs) provide rich structural and attribute information, but existing clustering methods struggle to jointly exploit multi-view attributes and multiple graph structures. Moreover, they often focus on either visible view collaboration or hidden feature extraction, failing to capture the synergy between the two. To address these challenges, we propose ITF-MVFC (Multi-View Fuzzy Clustering with Intrinsic and Topological Features), a novel clustering model designed for MVAGs. ITF-MVFC first constructs proximity matrices from topological connections and then introduces a Double Visible-Hidden Feature Extraction (DVHFE) mechanism based on non-negative matrix factorization (NMF). This extracts both intrinsic and topological visible-hidden feature representations. To enhance sparsity and interpretability, we further employ network lasso regularization, enabling effective cooperative learning between intrinsic and topological views. Finally, a fuzzy clustering objective function is established to integrate these multi-view representations. Experiments on synthetic, real-world, and large size datasets show that ITF-MVFC consistently outperforms state-of-the-art clustering methods on both external metrics and internal validation indices across multiple datasets.

Due to the increasing need to protect public security, this article studies the security traffic patrolling (STP) problem, where a collection of police officers plan to patrol around a city. In STP, the patrolling policy should not only take drivers’ opportunistic behaviors into consideration but also satisfy frequency constraints such that hot-spot regions are patrolled at least once every several periods. Existing randomized methods are efficient in reducing traffic law violations but can only satisfy the frequency constraints in a probabilistic manner. Traditional planning methods can be employed to meet the frequency constraints in a deterministic manner. However, it is difficult to find the deterministic patrolling paths for city-scale STP with hundreds of police officers and regions. Against this background, this article proposes a novel two-stage offline–online integration framework to guarantee frequency constraints while efficiently preventing traffic law violations of drivers. In the offline stage, a linear programming (LP)-based randomized policy is designed, where the patrolling efficiency is modeled as the objective and the frequency constraint is modeled in a probabilistic manner. Guided by the offline policy, in the online stage, by observing the real distribution of police officers, real-time planning is proposed to reschedule the police to guarantee the frequency constraints. Extensive empirical experiments on synthetic and real datasets are conducted to validate the proposed framework. The results demonstrate that compared with existing baseline solutions, the proposed two-stage STP framework can reduce the driver violation rate as much as possible, satisfy frequency constraints and scale well to STP in a real-time fashion.

Traffic signal control (TSC) has been one of the most useful ways for reducing urban road congestion. The challenge of TSC includes 1) real-time signal decision, 2) the complexity in traffic dynamics, and 3) the network-level coordination. Reinforcement learning (RL) methods can query policies by mapping the traffic state to the signal decision in real-time, however, are inadequate for different traffic flow environment. By observing real traffic information, online planning methods can compute the signal decisions in a responsive manner. Unfortunately, existing online planning methods either require high computation complexity or get stuck in local coordination. Against this background, we propose an explicit multiagent coordination (EMC)-based online planning methods that can satisfy adaptive, real-time and network-level TSC. By multiagent, we model each intersection as an autonomous agent, and the coordination efficiency is modeled by a cost function between neighbor intersections. By network-level coordination, each agent exchanges messages of cost function with its neighbors in a fully decentralized manner. By real-time, the message-passing procedure can interrupt at any time when the real time limit is reached and agents select the optimal signal decisions according to current message. Finally, we test our EMC method in both synthetic and real road network datasets. Experimental results are encouraging: compared to RL and conventional transportation baselines, our EMC method performs reasonably well in terms of adapting to real-time traffic dynamics, minimizing vehicle travel time and scalability to city-scale road networks.

Multiagent Markov decision processes (MMDPs) provide an expressive framework for multiagent planning in stochastic domains. However, exactly solving a large MMDP is often intractable due to the exponential space of joint action. Due to the trade-off nature of trading computation time for solution quality, decentralized subgoal-based tree search (Dec-SGTS) methods have shown great success for MMDPs. Existing Dec-SGTS methods rely on the predefined subgoals and the communication between agents to perform well, which might not hold in real-world MMDP domains where there is no expert knowledge on subgoals and communication resources are limited. In this paper, we relax these assumptions that arrive at an automated and communication-free Dec-SGTS. On the one hand, we first propose an upstream guided subgoal search (UGSS) technique to exploit historical search experience for subgoal discovery. On the other hand, in order to coordinate agents’ behaviors without communication, we further propose an expectation-alignment technique to proactively align agents’ policies with team’s expectations. Finally, we conduct extensive experiments on multirobot box-pushing tasks, and the results show that compared to multiagent planning benchmarks, the proposed communication-free Dec-SGTS method, which does not require any subgoal priors, achieves satisfactory rewards.

Knowledge-based clustering algorithms can improve traditional clustering models by introducing domain knowledge to identify the underlying data structure. While there have been several approaches to clustering with the guidance of knowledge tidbits, most of them mainly focus on numeric knowledge without considering the uncertain nature of information. To capture the uncertainty of information, pure numeric knowledge tidbits are expanded to knowledge granules in this article. Then, two questions arise: how to obtain granular knowledge and how to use those knowledge granules in clustering. To the end, a novel knowledge extraction and granulation (KEG) method and a granular knowledge-based fuzzy clustering model are proposed in this study. First, inspired by the concept of natural neighbors, an automatic KEG is developed. In KEG, high-density points are filtered from the dataset and then merged with their natural neighbors to form several dense areas, i.e., granular knowledge. Furthermore, the granular knowledge expressed by interval or triangular numbers is leveraged into the clustering algorithm, which is the framework of fuzzy clustering with granular knowledge. To concretize this model into clustering algorithms, the classical fuzzy C-Means clustering algorithm has been selected to incorporate the granular knowledge produced by KEG. Then, the corresponding fuzzy C-Means clustering with interval knowledge granules (IKG-FCM) and triangular knowledge granules (TKG-FCM) are proposed. Experiments on synthetic and real-world datasets demonstrate that IKG-FCM and TKG-FCM always achieve better clustering performance with less time cost, especially on imbalanced data, compared with state-of-the-art algorithms.

As an essential problem in robotics, foraging means that robots collect objects from a given environment and return them to a specified location. On many occasions, robots are required to perform foraging tasks in adversarial environments, such as battlefield rescue, where potential adversaries may damage robots with a certain probability. The longer an individual robot moves through adversarial environments, the higher the probability of being damaged by adversaries. The robot system can gain utility only when the robot brings carried objects back to a predetermined home station. Such a risk of being damaged makes returning home at different locations potentially relevant to the expected utility produced by the robot. Thus, the individual robot faces a dilemma when it responds to the potential risks in adversarial environments: whether to return the carried resources home or continue foraging tasks. In this article, two fundamental environment settings are discussed, homogeneous cases and heterogeneous cases. The former is analyzed as having both the optimal substructure property and the non-aftereffect property. Then, we present a dynamic programming (DP) algorithm that can find an optimal solution with polynomial time complexity. For the latter, it is proven that finding an optimal solution is \( \mathcal {NP} \) -hard. We then propose a heuristic algorithm: A division hierarchical path planning (DHPP) algorithm that is based on the idea of dividing the foraging routes generated initially into a certain number of subroutes to dilute risks. Finally, these algorithms are extensively evaluated in simulations, concluding that in adversarial environments, they can significantly improve the productivity of an individual robot before it is damaged.

While many voluntary movements involve bimanual coordination, few attempts have been made to simultaneously decode the trajectory of bimanual movements from electroencephalogram (EEG) signals. In this study, we proposed a novel bimanual brain-computer interface (BCI) paradigm to reconstruct the continuous trajectory of both hands during coordinated movements from EEG. The protocol required human subjects to complete a bimanual reaching task to the left, middle, or right target while EEG data were collected. A multi-task deep learning model combining the EEGNet and long short-term memory network (LSTM) was proposed to decode bimanual trajectories, including position and velocity. Decoding performance was evaluated in terms of the correlation coefficient (CC) and normalized root mean square error (NRMSE) between decoded and real trajectories. Experimental results from 13 human subjects showed that the grand-averaged combined CC values achieved 0.54 and 0.42 for position and velocity decoding, respectively. The corresponding combined NRMSE values were 0.22 and 0.23. Both CC and NRMSE were significantly superior to the chance level (p<0.05). Comparative experiments also indicated that the proposed model significantly outperformed some other commonly-used methods in terms of CC and NRMSE for continuous trajectory decoding. These findings demonstrated the feasibility of simultaneously decoding bimanual trajectory from EEG, indicating the potential of bimanual control for coordinated tasks.

With the increasing need of protecting public security, this paper studies the city-scale patrolling (CSP) problem, where hundreds of police officers are planned to patrol thousands of regions in a city. Given the stochastic nature with uncertain incident occurrence and travel time, the online CSP, where the patrolling policy should be determined sequentially, is of special interest. The online CSP aims at the omnipresence patrolling, which denotes that there are always police officers nearby that can respond timely when an incident occurs. Existing exact combinatorial optimization approaches are time-consuming and cannot scale to online CSP scenarios. On the other hand, within the dynamic CSP environments, existing decentralized multiagent coordination-based approaches always converge to the suboptimal solution without any performance guarantee. To achieve the omnipresence patrolling in a real-time fashion, a novel two-stage CSP framework is proposed. In the first stage, an offline model-based patrolling policy (OFFLINECSP) is designed, where the historical data is used to build the model and the linear programming (LP) technique is proposed for the coordination policy. Guided by the benchmark OFFLINECSP policy, an efficient online patrolling (ONLINECSP) policy is proposed in the second stage, where the police prefers to serve the critical incidents and to patrol hot-spot regions. The theoretical result shows that the competitive ratio of ONLINECSP can be guaranteed. Finally, extensive experiments on the synthetic and real datasets are conducted to validate the proposed framework. The results demonstrate that compared with existing benchmarks, the proposed two-stage CSP framework can not only maximize the incident service rate but also scale well to CSP in a real-time fashion.

Team formation has been extensively studied for complex task crowdsourcing in E-markets, in which a set of workers are hired to form a team to complete a complex task collaboratively. However, existing studies have two typical drawbacks: 1) each team is created for only one task, which may be costly and cannot accommodate crowdsourcing markets with a large number of tasks; and 2) most existing studies form teams in a centralized manner by the requesters, which may place a heavy burden on requesters. In fact, we observe that many complex tasks at real-world crowdsourcing platforms have similar skill requirements and workers are often connected through social networks. Therefore, this paper explores distributed team formation-based batch crowdsourcing for complex tasks to address the drawbacks in existing studies, in which similar tasks can be addressed in a batch to reduce computational costs and workers can self-organize through their social networks to form teams. To solve such an NP-hard problem, this paper presents two approaches: one is to form a fixed team for all tasks in the batch; the other is to form a basic team that can be dynamically adjusted for each task in the batch. In comparison, the former approach has lower computational complexity but the latter approach performs better in reducing the total payments by requesters. With the experiments on a real-world dataset comparing with previous benchmark approaches, it is shown that the presented approaches have better performance in saving the costs of forming teams, payments by requesters, and communication among team members; moreover, the presented approaches have higher success rate of tasks and much better scalability.

Existing studies on the multirobot foraging problem often assume safe settings, in which nothing in an environment hinders the robots’ tasks. In many real-world applications, robots have to collect objects from hazardous environments like earthquake rescue, where possible risks exist, with possibilities of destroying robots. At this stage, there are no targeted algorithms for foraging robots in hazardous environments, which can lead to damage to the robot itself and reduce the final foraging efficiency. A motivating example is a rescue scenario, in which the lack of a suitable solution results in many victims not being rescued after all available robots have been destroyed. Foraging robots face a dilemma after some robots have been destroyed: whether to take over tasks of the destroyed robots or continue executing their remaining foraging tasks. The challenges that arise when attempting such a balance are twofold: (1) the loss of robots adds new constraints to traditional problems, complicating the structure of the solution space, and (2) the task allocation strategy in a multirobot team affects the final expected utility, thereby increasing the dimension of the solution space. In this study, we address these challenges in two fundamental environmental settings: homogeneous and heterogeneous cases. For the former case, a decomposition and grafting mechanism is adopted to split this problem into two weakly coupled problems: the foraging task execution problem and the foraging task allocation problem. We propose an exact foraging task allocation algorithm, and graft it to another exact foraging task execution algorithm to find an optimal solution within the polynomial time. For the latter case, it is proven \( \mathcal {NP} \) -hard to find an optimal solution in polynomial time. The decomposition and grafting mechanism is also adopted here, and our proposed greedy risk-aware foraging algorithm is grafted to our proposed hierarchical agglomerative clustering algorithm to find high-utility solutions with low computational overhead. Finally, these algorithms are extensively evaluated through simulations, demonstrating that compared with various benchmarks, they can significantly increase the utility of objects returned by robots before all the robots have been stopped.

Motivated by the increasing need of the real-world patrolling, this paper studies a practical city-scale patrolling (CSP) variant. In CSP, the police are scheduled to patrol city regions, and the objective is not only to protect public security but also to respond to incidents timely. We use an integer program (IP) to formulate the CSP problem, with the objective of maximizing the police visibility rate (PVR) to improve public safety and the additional constraint of response time guarantee to handle incidents timely. For such an NP-hard problem, existing studies either cannot scale-up or do not provide a bound from optimum. To fill the research gap, we propose a decomposition and grafting approach. We first decompose the original CSP into two weakly-coupled subproblems, minimizing police problem (MinP) and maximizing PVR (MaxP) problem. By exploiting the subproblem structures, a polynomial time approximation algorithm is proposed for MinP, and a polynomial time optimal algorithm is proposed for MaxP. We prove that such a decomposition can provide the 1 - α approximation ratio, where α is the percentage of the police used in MinP. To further improve patrolling efficiency, a grafting mechanism is proposed to integrate the two subproblems' solutions. Finally, we conduct extensive experiments on the real dataset of Foshan, a modern Chinese city. The results demonstrate that compared with benchmarks, our approach scales well to city-scale problem instances with fine-grained periods, hundreds of regions, and hundreds of police officers.

Previous crowdsourcing studies often adopted the individual-oriented approach that outsources a task to an individual worker or team formation-based approach that outsources a task to an artificially formed team of workers. Nowadays, workers are often naturally organized into groups through social networks. To address such common issue of grouped workers in real crowdsourcing systems, this article explores a novel crowdsourcing paradigm in which the task allocation targets are naturally existing worker groups but not individual workers or artificially formed teams as before. Because a natural group might not possess all required skills and needs to coordinate with other groups in the social network contexts for performing a complex task, a concept of contextual crowdsourcing value is presented to measure a group's capacity to complete a task by coordinating with its contextual groups, which determines the priority that the group is assigned the task; then, the task allocation algorithms, including the allocations of groups and the workers actually participating in executing the task, are designed. The experiments on a real-world dataset show that our presented group-oriented approach can nearly always achieve better synergy performance, consistency performance, conflict performance, adaptability, and effectiveness on reducing costs, as compared with previous benchmark individual-oriented and team formation approaches.

Mobile social networks (MSNs) provide real-time information services to individuals in social communities through mobile devices. However, due to their high openness and autonomy, MSNs have been suffering from rampant rumors, fraudulent activities, and other types of misuses. To mitigate such threats, it is urgent to control the spread of fraud information. The research challenge is: how to design control strategies to efficiently utilize limited resources and meanwhile minimize individuals' losses caused by fraud information? To this end, we model the fraud information control issue as an optimal control problem, in which the control resources consumption for implementing control strategies and the losses of individuals are jointly taken as a constraint called total cost, and the minimum total cost becomes the objective function. Based on the optimal control theory, we devise the optimal dynamic allocation of control strategies. Besides, a dynamics model for fraud information diffusion is established by considering the uncertain mental state of individuals, we investigate the trend of fraud information diffusion and the stability of the dynamics model. Our simulation study shows that the proposed optimal control strategies can effectively inhibit the diffusion of fraud information while incurring the smallest total cost. Compared with other control strategies, the control effect of the proposed optimal control strategies is about 10% higher.

Peer grading is a natural crowdsourcing application, where dispersed students/peers resources are collected to evaluate others' assignments. Peer grading also offers a promising solution for scaling evaluation and learning to large-scale educational systems. A key challenge in peer grading is motivating peers to grade diligently and provide a high-quality evaluation. Spot-checking (SC) mechanisms, allowing instructors to check evaluations, can prevent peer collusion where peers grade arbitrarily and coordinate to report the uninformative grade. However, existing SC mechanisms unrealistically assume that peers have the same grading reliability and cost. This is limiting in practice, where we would expect peers to differ in reliability and cost. This article proposes the general Optimal SC (OptSC) model of determining the probability that each assignment needs to be checked to maximize assignments' evaluation accuracy aggregated from peers and takes into consideration: 1) peers' heterogeneous characteristics and 2) peers' strategic grading behaviors to maximize their own utility. We prove that the bilevel OptSC is NP-hard to solve. By exploiting peers' grading behaviors, we first formulate a single-level relaxation to approximate OptSC. By further exploiting structural properties of the relaxed problem, we propose an efficient algorithm to that relaxation, which also gives a good approximation of the original OptSC. Extensive experiments on both synthetic and real data sets show significant advantages of the proposed algorithm over existing approaches.

There are two problems in the traditional crowdsourcing systems for handling complex tasks. First, decomposing complex tasks into a set of micro-subtasks requires the decomposition capability of the requesters; thus, some requesters may abandon using crowdsourcing to accomplish a large number of complex tasks since they cannot bear such heavy burden by themselves. Second, tasks are often assigned redundantly to multiple workers to achieve reliable results, but reliability may not be ensured when there are many malicious workers in the crowd. Currently, it is observed that the workers are often connected through social networks, a feature that can significantly facilitate task allocation and task execution in crowdsourcing. Therefore, this paper investigates crowdsourcing in social networks and presents a novel context-aware reliable crowdsourcing approach. In our presented approach, the two problems in traditional crowdsourcing are addressed as follows: 1) the complex tasks can be performed through autonomous coordination between the assigned worker and his contextual workers in the social network; thus, the requesters can be exempt from a heavy computing load for decomposing complex tasks into subtasks and combing the partial results of subtasks, thereby enabling more requesters to accomplish a large number of complex tasks through crowdsourcing, and 2) the reliability of a worker is determined not only by the reputation of the worker himself but also by the reputations of the contextual workers in the social network; thus, the unreliability of transient or malicious workers can be effectively addressed. The presented approach addresses two types of social networks including simplex and multiplex networks. Based on theoretical analyses and experiments on a real-world dataset, we find that the presented approach can achieve significantly higher task allocation and execution efficiency than the previous benchmark task allocation approaches; moreover, the presented contextual reputation mechanism can achieve relatively higher reliability when there are many malicious workers in the crowd.

With the increasing complexity of tasks that are crowdsourced, requesters need to form teams of professional workers that can satisfy complex task skill requirements. Team crowdsourcing in social networks (SNs) provides a promising solution for complex task crowdsourcing, where the requester hires a team of professional workers that are also socially connected can work together collaboratively. Previous social team formation approaches have mainly focused on the algorithmic aspect for social welfare maximization; however, within the traditional objective of maximizing social welfare alone, selfish workers can manipulate the crowdsourcing market by behaving untruthfully. This dishonest behavior discourages other workers from participating and is unprofitable for the requester. To address this strategic social team crowdsourcing problem, truthful mechanisms are developed to guarantee that a worker's utility is optimized when he behaves honestly. This problem is proved to NP-hard, and two efficient mechanisms are proposed to optimize social welfare while reducing time complexity for different scale applications. For small-scale applications where the task requires a small number of skills, a binary tree network is first extracted from the social network, and a dynamic programming-based optimal team is formed in the binary tree. For large-scale applications where the task requires a large number of skills, a team is formed greedily based on the workers' social structure, skill, and working cost. For both mechanisms, the threshold payment rule, which pays each worker his marginal value for task completion, is proposed to elicit truthfulness. Finally, the experimental results of a real-world dataset show that compared to the benchmark exponential VCG truthful mechanism, the proposed small-scale-oriented mechanism can reduce computation time while producing nearly the same social welfare results. Furthermore, compared to other state-of-the-art polynomial heuristics, the proposed large-scale-oriented mechanism can achieve truthfulness while generating better social welfare outcomes.

Existing studies on crowdsourcing often adopt the retail-style allocation approach, in which tasks are allocated individually and independently. However, such retail-style task allocation has the following problems: 1) each task is executed independently from scratch, thus the execution of one task seldom utilize the results of other tasks and the requester must pay in full for the task; 2) many workers only undertake a very small number of tasks contemporaneously, thus the workers' skills and time may not be fully utilized. We observe that many complex tasks in real-world crowdsourcing platforms have similar skill requirements and long deadlines. Based on these real-world observations, this paper presents a novel batch allocation approach for tasks with overlapping skill requirements. Requesters' real payment can be discounted because the real execution cost of tasks can be reduced due to batch allocation and execution, and each worker's real earnings may increase because he/she can undertake more tasks contemporaneously. This batch allocation optimization problem is proved to be NP-hard. Then, two types of heuristic approaches are designed: layered batch allocation and core-based batch allocation. The former approach mainly utilizes the hierarchy pattern to form all possible batches, which can achieve better performance but may require higher computational cost since all possible batches are formed and observed; the latter approach selects core tasks to form batches, which can achieve suboptimal performance with lower complexity and significantly reduce computational cost. With the theoretical analyses and experiments on a real-world Upwork dataset in which the proposed approaches are compared with the previous benchmark retail-style allocation approach, we find that our approaches have better performances in terms of total payment by requesters and average income of workers, as well as maintaining close successful task completion probability and consuming less task allocation time.

Peer grading, allowing students/peers to evaluate others' assignments, offers a promising solution for scaling evaluation and learning to large-scale educational systems. A key challenge in peer grading is motivating peers to grade diligently. While existing spot-checking (SC) mechanisms can prevent peer collusion where peers coordinate to report the uninformative grade, they unrealistically assume that peers have the same grading reliability and cost. This paper studies the general Optimal Spot-Checking (OptSC) problem of determining the probability each assignment needs to be checked to maximize assignments' evaluation accuracy aggregated from peers, and takes into consideration 1) peers' heterogeneous characteristics, and 2) peers' strategic grading behaviors to maximize their own utility. We prove that the bilevel OptSC is NP-hard to solve. By exploiting peers' grading behaviors, we first formulate a single level relaxation to approximate OptSC. By further exploiting structural properties of the relaxed problem, we propose an efficient algorithm to that relaxation, which also gives a good approximation of the original OptSC. Extensive experiments on both synthetic and real datasets show significant advantages of the proposed algorithm over existing approaches.

Crowdsourcing has recently been significantly explored. Although related surveys have been conducted regarding this subject, each has mainly consisted of a review of a single aspect of crowdsourcing systems or on the application of crowdsourcing in a specific application domain. A crowdsourcing system is a comprehensive set of multiple entities, including various elements and processes. Multiagent computing has already been widely envisioned as a powerful paradigm for modeling autonomous multi-entity systems with adaptation to dynamic environments. Therefore, this article presents a novel multiagent perspective and approach to understanding crowdsourcing systems, which can be used to correlate the research on crowdsourcing and multiagent systems and inspire possible interdisciplinary research between the two areas. This article mainly discusses the following two aspects: (1) The multiagent perspective can be used for conducting a comprehensive survey on the state of the art of crowdsourcing, and (2) the multiagent approach can bring about concrete enhancements for crowdsourcing technology and inspire future research directions that enable crowdsourcing research to overcome the typical challenges in crowdsourcing technology. Finally, this article discusses the advantages and disadvantages of the multiagent perspective by comparing it with two other popular perspectives on crowdsourcing: the business perspective and the technical perspective.

In crowdsourcing markets, a requester announces a task and calls for contribution from potential participants. With strategic participants, the requester needs to reward the participants to introduce the incentives of participation. However, it is natural to ask whether it is worth introducing incentives if the total payment for eliciting incentives is too high. This paper addresses such a fundamental concern by designing a frugal mechanism with minimum payment used to procure the total amount of service contributions demanded. We design two mechanisms to provide the incentives of participation while minimizing the payment used by the requester. We first propose a frugal auction-based mechanism, which stimulates participants to truthfully report their information. We theoretically prove that the payment used is not more than the optimal cost (with no incentive considered) plus a bounded additive. We then design a Stackelberg-game-based mechanism, in which the requester fixes a certain total payment at the very beginning so as to encourage the participants to compete for it and participate in the task. We verify the existence of a unique Nash equilibrium (NE) and develop a novel algorithm to find the NE, as well as the optimal payment to extract the NE. Our simulation results show that the payment used in these mechanisms is close to the optimal solution with no incentive considered, while the extra payment caused by introducing truthfulness in auction-based mechanism is about twice that of the NE in Stakelberg-game-based mechanism.

Crowdsourcing has become a popular service computing paradigm for requesters to integrate the ubiquitous human-intelligence services for tasks that are difficult for computers but trivial for humans. This paper focuses on crowdsourcing complex tasks by team formation in social networks (SNs) where a requester connects to a large number of workers. A good indicator of efficient team collaboration is the social connection among workers. Most previous social team formation approaches, however, either assume that the requester can maintain information of all workers and can directly communicate with them to build teams, or assume that the workers are cooperative and be willing to join the specific team built by the requester, both of which are impractical in many real situations. To this end, this paper first models each worker as a selfish entity, where the requester prefers to hire inexpensive workers that require less payment and workers prefer to join the profitable teams where they can gain high revenue. Within the noncooperative SNs, a distributed negotiation-based team formation mechanism is designed for the requester to decide which worker to hire and for the worker to decide which team to join and how much should be paid for his skill service provision. The proposed social team formation approach can always build collaborative teams by allowing team members to form a connected graph such that they can work together efficiently. Finally, we conduct a set of experiments on real dataset of workers to evaluate the effectiveness of our approach. The experimental results show that our approach can: 1) preserve considerable social welfare by comparing the benchmark centralized approaches and 2) form the profitable teams within less negotiation time by comparing the traditional distributed approaches, making our approach a more economic option for real-world applications.

Cloud computing has emerged as a very flexible service paradigm by allowing users to require virtual machine (VM) resources on-demand and allowing cloud service providers (CSPs) to provide VM resources via a pay-as-you-go model. This paper addresses the CSP's problem of efficiently allocating VM resources to physical machines (PMs) with the aim of minimizing the energy consumption. Traditional energy-aware VM allocations either allocate VMs to PMs in a centralized manner or implement VM migrations for energy reduction without considering the migration cost in cloud computing systems. We address these two issues by introducing a decentralized multiagent (MA)-based VM allocation approach. The proposed MA works by first dispatching a cooperative agent to each PM to assist the PM in managing VM resources. Then, an auction-based VM allocation mechanism is designed for these agents to decide the allocations of VMs to PMs. Moreover, to tackle system dynamics and avoid incurring prohibitive VM migration overhead, a local negotiation-based VM consolidation mechanism is devised for the agents to exchange their assigned VMs for energy cost saving. We evaluate the efficiency of the MA approach by using both static and dynamic simulations. The static experimental results demonstrate that the MA can incur acceptable computation time to reduce system energy cost compared with traditional bin packing and genetic algorithm-based centralized approaches. In the dynamic setting, the energy cost of the MA is similar to that of benchmark global-based VM consolidation approaches, but the MA largely reduces the migration cost.

In a distributed service-oriented multiagent system, agents have to cooperate with each other to complete decentralized service discovery tasks. Since system structure can influence the efficiency of service discovery, structural self-organization mechanism should be used to facilitate the decentralized service discovery in the system. During decentralized service discovery processes, different agents will present different intermediary utilities, and the intermediary utilities of agents imply the distribution information of system services in a certain extent. Therefore, the intermediary utility can be used to improve the efficiency of structural self-organization and service search. However, the existing self-organization based service discovery approaches ignore this issue. In this paper, we propose a novel self-organization based service discovery approach considering intermediary utility. The approach addresses the intermediary utilities of agents in both the structural self-organization mechanism and service search strategy. Besides, this paper considers maintaining the global connectivity of system structure during concurrent self-organization processes to guarantee the global accessibility of services. To the best of our knowledge, the proposed approach is the first self-organization based service discovery approach that can provably preserve the global connectivity of system structure. The experimental results show that the proposed approach outperforms the previous approaches and considerably improves the success rate and search efficiency of service discovery.

Distributed and dynamic networks are ubiquitous in many real-world applications. Due to the huge-scale, decentralized, and dynamic characteristics, the global topological view is either too hard to obtain or even not available. So, most existing community detection methods working on the global view fail to handle such decentralized and dynamic large networks. In this paper, we propose a novel autonomy-oriented computing-based method for community mining (AOCCM) from the multiagent perspective in the distributed environment. In particular, AOCCM utilizes reactive agents to pick the neighborhood node with the largest structural similarity as the candidate node, and thus determine whether it should be added into local community based on the modularity gain. We further improve AOCCM to a more efficient incremental version named AOCCM-i for mining communities from dynamic networks. AOCCM and AOCCM-i can be easily expanded to detect both nonoverlapping and overlapping global community structures. Experimental results on real-life networks demonstrate that the proposed methods can reduce the computational cost by avoiding repeated structural similarity calculation and can still obtain the high-quality communities.

In past decades, significant attention has been devoted to the task allocation and load balancing in distributed systems. Although there have been some related surveys about this subject, each of which only made a very preliminary review on the state of art of one single type of distributed systems. To correlate the studies in varying types of distributed systems and make a comprehensive taxonomy on them, this survey mainly categorizes and reviews the representative studies on task allocation and load balancing according to the general characteristics of varying distributed systems. First, this survey summarizes the general characteristics of distributed systems. Based on these general characteristics, this survey reviews the studies on task allocation and load balancing with respect to the following aspects: 1) typical control models; 2) typical resource optimization methods; 3) typical methods for achieving reliability; 4) typical coordination mechanisms among heterogeneous nodes; and 5) typical models considering network structures. For each aspect, we summarize the existing studies and discuss the future research directions. Through the survey, the related studies in this area can be well understood based on how they can satisfy the general characteristics of distributed systems.

In multiplex networks, agents are connected by multiple types of links; a multiplex network can be split into more than one network layer that is composed of the same type of links and involved agents. Each network link type has a bias for communicating different types of resources; thus, the task’s access to the required resources in multiplex networks is strongly related to the network link types. However, traditional task allocation and load balancing methods only considered the situations of agents themselves and did not address the effects of network link types in multiplex networks. To solve this problem, this article considers both link types and agents, and substantially extends the existing work by highlighting the effect of network layers on task allocation and load balancing. Two multiplex network-adapted models of task allocation with load balancing are presented: network layer-oriented allocation and agent-oriented allocation. This article also addresses the unreliability in multiplex networks, which includes the unreliable links and agents, and implements a reliable task allocation based on a negotiation reputation and reward mechanism. Our findings show that both of our presented models can effectively and robustly satisfy the task allocation objectives in unreliable multiplex networks; the experiments prove that they can significantly reduce the time costs and improve the success rate of tasks for multiplex networks over the traditional simplex network-adapted task allocation model. Lastly, we find that our presented network layer-oriented allocation performs much better in terms of reliability and allocation time compared to our presented agent-oriented allocation, which further explains the importance of network layers in multiplex networks.

In recent years, significant attention has been paid to diffusion in social networks (SNs), which is, factually, the collective behavior of a set of autonomous social actors for interacting on something in SNs (such as opinions, viruses, or innovations). While this subject has been intensively reported, there have been relatively few systematic reviews concerning the typical diffusion elements and models that are relevant to this subject. Because multiagent computing has already been widely envisioned to be a powerful paradigm for modeling the collective interactions of autonomous multientity systems. In this survey, we review diffusion in SNs through a multiagent perspective. First, we review the following essential elements in diffusion: 1) diffusion actors (who will diffuse), which can be understood to be the interacting agents; 2) diffusion media (where to be diffused), which can be understood to be the interaction environments in multiagent systems (MASs); and 3) diffusion contents (what to be diffused), which can be understood to be the interaction objects in MASs. Next, based on varying situations of diffusion elements, we review the representative diffusion models (how to diffuse), which can be understood as the decision-making mechanisms and interaction protocols in MASs. For each class of diffusion elements and models, we summarize the existing studies and discuss the challenges for solving the complex diffusion problems by applying multiagent methodologies. Finally, we discuss the advantages and disadvantages of our multiagent perspective by comparing other typical perspectives (the empirical research perspective and the theoretical perspective in empirical research), and we conclude with suggestions for further research.

In online social network, the personal information dissemination behavior is reported to be affected by the clash of social individuals' biased opinions. In this paper, we present a model to discuss the influence of individual biased opinion on diffusion dynamics. Based on multi-agent simulations, we obtain some conclusions which are helpful for recommender systems and in controlling diffusion. In addition, our study offers potential avenues for the study of diffusion dynamics with personal biases.

Team formation is an effective collaboration manner in social networks (SNs). Within teams, social individuals can work together to accomplish complex jobs that they are unable to perform individually. Due to its wide range of applications, team formation in SNs has been studied extensively and a number of approaches have been proposed. However, all of these proposals either build teams of individuals to accomplish jobs through a centralized manner or ignore the selfish nature of social individuals, or both. In this paper, we introduce a decentralized negotiation-based team formation model for non-cooperative SNs, where social individuals are self-interested. The proposed team formation model works by allowing the employer (i.e., Job initiator) to recruit a team of professional employees that demand small working remuneration and incur little communication overhead and allowing the employees to join the beneficial teams from which they can achieve a high financial remuneration. The simulation results show that our model achieves about 80% social welfare of the ideal centralized models on average. Moreover, compared to other conventional distributed models, our model can reduce team formation time significantly, making our model a better choice for the real-world time-sensitive applications.

In multiagent systems, agents with limited capacity often cooperate in order to accomplish various types of tasks. Due to the openness of multiagent systems, agents may run the risk of their cooperations to accomplish tasks because of some involved undependable agents. The state of the art for handling this problem concentrates on the phase of task allocation, which aims to allocate tasks to more dependable agents (task allocation-oriented). In fact, accomplishing a task in a multiagent system requires two phases: i) task allocation, and ii) task execution. Hence, this paper, on the contrary, focuses on managing the phase of task execution to guarantee the performance of task accomplishment (task execution-oriented). To reduce the performance loss caused by undependable agents, the proposed agent division and fusion mechanism enables agents to autonomously divide themselves into sub-agents for executing tasks with different risks (more resources will be assigned to the sub-agent with lower risk in task execution), then the sub-agents can also fuse together and make a re-division to fit the current task environments. This work is also expected to be able to complement the existing state of the art (task allocation-oriented) for guaranteeing the performance of task accomplishment in undependable multiagent systems from task execution-oriented perspective.

In this paper, we propose a novel community-aware task allocation model for social networked multiagent systems (SN-MASs), where the agent' cooperation domain is constrained in community and each agent can negotiate only with its intracommunity member agents. Under such community-aware scenarios, we prove that it remains NP-hard to maximize system overall profit. To solve this problem effectively, we present a heuristic algorithm that is composed of three phases: 1) task selection: select the desirable task to be allocated preferentially; 2) allocation to community: allocate the selected task to communities based on a significant task-first heuristics; and 3) allocation to agent: negotiate resources for the selected task based on a nonoverlap agent-first and breadth-first resource negotiation mechanism. Through the theoretical analyses and experiments, the advantages of our presented heuristic algorithm and community-aware task allocation model are validated. 1) Our presented heuristic algorithm performs very closely to the benchmark exponential brute-force optimal algorithm and the network flow-based greedy algorithm in terms of system overall profit in small-scale applications. Moreover, in the large-scale applications, the presented heuristic algorithm achieves approximately the same overall system profit, but significantly reduces the computational load compared with the greedy algorithm. 2) Our presented community-aware task allocation model reduces the system communication cost compared with the previous global-aware task allocation model and improves the system overall profit greatly compared with the previous local neighbor-aware task allocation model.

Social networks have recently been widely explored in many fields; these networks are composed of a set of autonomous social actors and the interaction relations among them. Multiagent computing has already been widely envisioned to be a powerful paradigm for modeling autonomous multientity systems; therefore, it is promising to connect the research on social networks and multiagent systems. In general, there are three views for research on social networks: the structure-oriented view, in which only the network structure characteristics among actors are considered, the actor-oriented view, in which only the behavior characteristics of actors are considered, and the actor-structure crossing view, in which both actors and network structures are considered and their crossing effects are explored. This survey paper mainly concerns studies on social networks that have the last two views and discusses the relationship between social networks and multiagent systems. Because coordination is critical for both multiagent systems and social networks, this paper classifies studies on social networks that are based on the coordination mechanisms among the actors in the social networks. By referring to typical types of coordination situations in multiagent systems, social networks in previous studies can be classified into three classes: cooperative social networks, noncooperative social networks, and multiple social networks; for each class, this paper reviews the existing studies and discusses the challenge issues and possible future research directions. From this survey, we find that social networks can be understood well via a multiagent coordination perspective and also that many multiagent coordination techniques can be cogently applied in research on social networks. Moreover, this paper discusses the advantages and disadvantages of the multiagent coordination perspective by comparing with other perspectives on studying social networks.

In a multiplex network, agents are connected by multiple types of links, and the network can be split into more than one network layer which is composed of the same type of links and involved agents. Traditional task allocation methods of multiagent systems only consider the situations of agents themselves, but neglect the effects of network layers in multiplex networks. To solve such a problem, this paper takes network layers into account and presents a novel network layer-oriented task allocation model for multiplex agent networks, with such a model, first the network layers that can satisfy the objectives of task allocation will be allocated, then the final agents will be selected from the allocated network layers. Moreover, this paper deals with the situation of undependable networks, where the resource access of tasks may be undependable, and implements a task allocation based on negotiation reputation. It shows that the network layer-oriented task allocation model leads to an improvement in the success rate and execution time of tasks in multiplex networks when compared to traditional agent-oriented task allocation methods, moreover, such a model has good scalability for the size of tasks and robustness for dynamic undependability.

In the past, many approaches have been devised to address the load balancing problem for social networked multiagent systems (SN-MASs). However, few of these approaches consider the migration cost incurred when migrating tasks for load balancing, moreover, current SN-MASs often consist of communities, and the migration costs of intra-community and intercommunity transfers are heterogeneous. To minimize the load imbalance of agents and to incur the least migration cost, this paper introduces a net profit-based load balancing mechanism. In this mechanism, each load balance process (i.e., migrating a task from one agent to another agent) is associated with a net profit value which depends on the benefit it gains by making a contribution to alleviating the system load unfairness and the cost of migrating the task. The agents always perform the optimal load balance process that has the maximum net profit value, thereby improving system performance, as well as reducing the migration cost. Our simulations show that our approach not only guarantees that agents can undertake fair loads but also reduces the overhead migration costs compared with the previous load balancing approaches that ignore the cost of migrating the task.

Task execution of multiagent systems in social networks (MAS-SN) can be described through agents' operations when accessing necessary resources distributed in the social networks; thus, task allocation can be implemented based on the agents' access to the resources required for each task and aimed to minimize this resource access time. Currently, in undependable MAS-SN, there are deceptive agents that may fabricate their resource status information during task allocation but not really contribute resources to task execution; although there are some game theory-based solutions for undependable MAS, but which do not consider minimizing resource access time that is crucial to the performance of task execution in social networks. To achieve dependable resources with the least access time to execute tasks in undependable MAS-SN, this paper presents a novel task allocation model based on the negotiation reputation mechanism, where an agent's past behaviors in the resource negotiation of task execution can influence its probability to be allocated new tasks in the future. In this model, the agent that contributes more dependable resources with less access time during task execution is rewarded with a higher negotiation reputation, and may receive preferential allocation of new tasks. Through experiments, we determine that our task allocation model is superior to the traditional resources-based allocation approaches and game theory-based allocation approaches in terms of both the task allocation success rate and task execution time and that it usually performs close to the ideal approach (in which deceptive agents are fully detected) in terms of task execution time.

In networked multiagent systems (NMASs) with resource caching, resource replicas are cached in favor of the agents who accessed such resources most recently and frequently. Task execution in NMASs is described through agents' operations when accessing necessary resources distributed in the networks, and thus, agents with richer experiences executing tasks will have higher access to resources. To optimize tasks' resource access time, we investigate two types of preferential attachments in the task allocation of NMASs with resource caching: history and present preferential attachments, in which an agent has higher access to a resource if that agent has richer history (or present) accessing experiences for that resource. Therefore, agents that were (or are) heavily burdened by tasks may have certain preferential rights to new tasks in the future. Our experiments found that preferential attachment in task allocation can effectively reduce tasks' execution time, particularly when the network context is considered and the number of tasks is high. In addition, we discovered two interesting phenomena: (1) Compromise between preferential attachment and load balancing can achieve better performance than single preferential attachment when there are too many tasks waiting, and (2) the integration of history and present preferential attachments can outperform either history or present preferential attachment alone in task allocation.

Networked multiagent systems are very popular in large-scale application environments. In networked multiagent systems, the interaction structures can be shaped into the form of networks where each agent occupies a position that is determined by such agent's relations with others. To avoid collisions between agents, the decision of each agent's strategies should match its own interaction position, so that the strategies available to all agents are in line with their interaction structures. Therefore, this paper presents a novel decision-making model for networked multiagent strategies based on their interaction structures, where the set of strategies for an agent is conditionally decided by other agents within its dependence interaction substructure. With the presented model, the resulting strategies available to all agents can minimize the collisions of multiagents regarding their interaction structures, and the model can produce the same resulting strategies for the isomorphic interaction structures. Furthermore, this paper uses a multiagent citation network as a case study to demonstrate the effectiveness of the presented decision-making model.

In the complex software systems, software agents always need to negotiate with other agents within their physical and social contexts when they execute tasks. Obviously, the capacity of a software agent to execute tasks is determined by not only itself but also its contextual agents; thus, the number of tasks allocated on an agent should be directly proportional to its self-owned resources as well as its contextual agents' resources. This paper presents a novel task allocation model based on the contextual resource negotiation. In the presented task allocation model, while a task comes to the software system, it is first assigned to a principal agent that has high contextual enrichment factor for the required resources; then, the principal agent will negotiate with its contextual agents to execute the assigned task. However, while multiple tasks come to the software system, it is necessary to make load balancing to avoid overconvergence of tasks at certain agents that are rich of contextual resources. Thus, this paper also presents a novel load balancing method: if there are overlarge number of tasks queued for a certain agent, the capacities of both the agent itself and its contextual agents to accept new tasks will be reduced. Therefore, in this paper, the task allocation and load balancing are implemented according to the contextual resource distribution of agents, which can be well suited for the characteristics of complex software systems; and the presented model can reduce more communication costs between allocated agents than the previous methods based on self-owned resource distribution of agents.

Strategy diffusion is a common phenomenon in the collective motion of multiagents, which is the large scale of strategy penetrations of certain agents on other agents; there are many kinds of diffusion forms; among them, the collective diffusion is always seen, which implies that a social strategy accepted by collective agents may have strong authority and tend to diffuse to other agents. This paper presents a novel spatial model for the collective strategy diffusion in multiagent societies. In the model, the social distance between agents can be measured in a Euclidian space; the authority of a social strategy is determined by not only the number but also the collective social positions of its overlaid agents; social strategies that have strong authorities are impressed on the other agents, and the agents will accept (partially or in full) or reject them based on their own social strategies and social positions. Moreover, the paper also considers the concurrent form in the collective diffusion and presents that an agent's social strategy is influenced not only by the diffusion that bears on itself but also by other concurrent diffusion processes that bear on other agents, and an agent will incline to the average social strategy of the whole system, which can make the system more unified. Finally, the paper uses queue orientation as a case to study the presented model.

In agent migration process, malicious hosts can compromise the agent. To solve the problem, the paper introduces the measure of agent integrity verification and constructs a novel agent fault-tolerant migration model. The model avoids much agent replicas in migration process. By simulation experiment, the results prove that the model provided by the paper is feasible and efficient, and can save network resource much than other relative works.

Access control is the process of mediating every request to resource and data maintained by an active node system and determining whether the request should be granted or denied. In This work we present an access control policy called family tree policy. The family tree policy can correctly represent active network that cannot be correctly modeled by BLP and Chinese wall model. In the family tree policy, the subjects and objects of the system are classified as different Inheriting classes. A subject cannot access the object of the different inheriting class. In the same inheriting class, the subject and object abide by the BLP model. All different inheriting classes have the same ancestor. The ancestor can access any inheriting class and comply with BLP model.