Current AI agents in wargaming often rely on preset rules and historical data, limiting their adaptability in complex and dynamic scenarios. This highlights the need to better understand human decision-making. This study employs eye-tracking technology to investigate human decision processes and cognitive load in wargames. By analyzing eye movement data, we aim to understand the decision patterns of human players. Six key eye movement metrics are identified as effective indicators of decision complexity in wargaming tasks. Then, we develop a deep neural network based on these metrics, achieving an accuracy of 88.49% in detecting players' current decision tasks, significantly outperforming traditional models like Random Forest (86.0%) and GBDT (83.97%). This research demonstrates the intrinsic connection between eye movement features and decision-making tasks, offering valuable insights for designing human-aligned AI agents and enhancing perception in human-AI collaborative gameplay.

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.

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.

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.

This paper introduces a novel paradigm for building trustworthy autonomous systems through explanation-driven adaptation. We propose a co-adaptive architecture where black-box neural policies, interpretable policy distillation models and explanation engines evolve synergistically through bidirectional feedback loops. The framework fundamentally transforms explainability from passive post-analysis to active system guidance, enabling continuous policy optimization while maintaining human-aligned transparency. By embedding explanation consistency as a core adaptation objective, our approach establishes dynamic equilibrium between environmental responsiveness and operational verifiability. This work advances the design of safety-critical autonomous systems through formalized principles for explainability-guided adaptation, creating new pathways for resilient human-machine collaboration in complex service environments.

Traditionally, economic models have often been based on fixed assumptions and analyzed within static scenarios. Economists have long sought a more flexible and dynamically adaptive model to find more precise models to represent socioeconomic laws. In this study, we built an economic engine grounded in multi-agent intelligent game simulations and employed reinforcement learning (RL) techniques to simulate economic activities. By establishing diverse agent behaviors across three industries and iterating taxation adjustment policies, we simulated and deduced socio-economic activities. Our simulation experiments confirmed that multi-agent intelligent game simulations, underpinned by reinforcement learning, can to some extent capture valuable economic operational patterns. The AI tax table, derived from the evolutionary economic engine, proves to be more realistic; it can boost overall societal production while ensuring maximal social fairness, thus finding a valuable local Nash equilibrium.

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.

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.

Wargame has emerged as a preferred instrument for simulating combat decision-making. This paper employs machine learning methodologies to predict the outcome of wargame matches. Initially, we conducted data preprocessing on 335 wargame match replays, extracting and generating features from both macro and micro perspectives, thereby capturing player strategies and operational nuances. This meticulous process culminated in the formation of a comprehensive player behavioral feature dataset. Subsequently, we harnessed six distinct machine learning models to prognosticate match results in the domain of wargaming using this dataset, achieving a peak prediction accuracy of 96.11%. The primary emphasis lies in the identification of prevalent determinants contributing to player triumphs in wargaming. To this end, we conducted an attribution analysis to ascertain the significance of diverse macro and micro features. Guided by the importance of these features, we propose a method for evaluating player performance. This methodology can be instrumental in scrutinizing disparate player wargaming styles, dissecting customary strategic behaviors that lead to player victories, and assisting wargame designers in crafting AI agents capable of adapting to a spectrum of human player behaviors. Consequently, this study offers substantial insights for the advancement of research in the realm of human-AI hybrid gameplay.

With the development of society, intelligent games have gradually become a hot research field. This article proposes an algorithm that combines the multiattribute decision-making and reinforcement learning methods to apply to multiagents’ decision-making for wargaming artificial intelligence (AI).This algorithm solves the problem of the agent's low rate of winning against specific rules and its inability to quickly converge during intelligent wargame training. At the same time, a multiattribute decision-making method based on the entropy–weight method was proposed to obtain the normalized weighting for each attribute that feeds into a deep reinforcement learning model. A simulation experiment confirms that the real-number multiattribute decision-making-proximal policy optimization (PPO) algorithm of multiattribute decision-making combined with reinforcement learning presented in this article is significantly more intelligent than the pure reinforcement learning algorithm.

Presently, intelligent games have emerged as a substantial research area. Nonetheless, the slow convergence of intelligent wargame training and the low success rates of agents against specific rules present challenges. In this article, we propose a game confrontation algorithm combining the multiple attribute decision making (MADM) approach from management science and reinforcement learning (RL) technology. This integration enables us to combine the strengths of both approaches and addresses the above issues effectively. This study conducts experiments using the algorithm that integrates MADM and RL techniques to gather confrontation data from the red and blue sides within the winning-first wargame platform. The data is then analyzed using the weight calculation method of intuitionistic fuzzy numbers to determine each intelligent opponent agent's threat level from the perspective of MADM. The threat level calculated by MADM is used to construct the reward function for the red side. The simulation results demonstrate that the algorithm combining MADM and RL proposed in this study outperforms classical RL algorithms regarding intelligence. This approach effectively addresses issues, such as the convergence difficulty, caused by random initialization and the sparse rewards for agent neural networks in wargame environments with large maps. Combining the MADM method from management with the RL algorithm in control can lead to cross-disciplinary innovation in academic fields, which provides innovative research values for intelligent wargame design and RL algorithm improvements.

Due to the effectiveness and advantages of interval-valued intuitionistic fuzzy sets (IVIFSs) in evaluating uncertainty and risk, we introduce IVIFSs into loss functions of decision-theoretic rough sets (DTRSs) and propose an optimization-based approach to interval-valued intuitionistic fuzzy three-way decisions. First, based on the classical DTRSs and two previous optimization models, we construct a new concise linear programming model for simultaneously determining the threshold pair. Our model is mathematically equivalent to the DTRSs and the previous models under the Karush-Kuhn-Tucker (KKT) condition. Second, we extend the constructed model via the IVIFSs of loss functions and we discuss the relations between these loss functions based on a similarity measure function-based ranking method and a multiple score function-based ranking method for IVIFSs. Third, we develop our extended models via two ranking methods and we prove the existence and uniqueness of the optimal solution of the model. The optimization-based method, along with its algorithm for three-way decisions, is designed in an interval-valued intuitionistic fuzzy environment. Compared to the latest existing methods, our method has three advantages (see Advantages 1-3). Finally, an illustrative example is considered, and the advantages of our approach are demonstrated by this example.

The application of artificial intelligence (AI) in games has been significantly developed and attracted much attention over the past few years. This article not only leverages the reinforcement learning multiagent deep deterministic policy gradient algorithm to realize the dynamic decision-making of game AI but also creatively incorporates deep learning and natural language processing technologies in the wargame field to transform game context situation maps into textual suggestions in wargame confrontation. In this article, we effectively integrate reinforcement learning technologies, deep learning technologies, and natural language processing technologies to generalize the semantic text output at state-of-the-art accuracy, which plays an important role in human understanding of game AI behavior. The experimental results are promising and can be used to verify the feasibility, accuracy, and performance of our proposed model in extensive simulations against benchmarking methods.

Regression analysis based methods have shown strong robustness and achieved great success in face recognition. In these methods, convex$l_1$-norm and nuclear norm are usually utilized to approximate the$l_0$-norm and rank function. However, such convex relaxations may introduce a bias and lead to a suboptimal solution. In this paper, we propose a novel Enhanced Group Sparse regularized Nonconvex Regression (EGSNR) method for robust face recognition. An upper bounded nonconvex function is introduced to replace$l_1$-norm for sparsity, which alleviates the bias problem and adverse effects caused by outliers. To capture the characteristics of complex errors, we propose a mixed model by combining$\gamma$-norm and matrix$\gamma$-norm induced from the nonconvex function. Furthermore, an$l_{2,\gamma }$-norm based regularizer is designed to directly seek the interclass sparsity or group sparsity instead of traditional$l_{2,1}$-norm. The locality of data, i.e., the distance between the query sample and multi-subspaces, is also taken into consideration. This enhanced group sparse regularizer enables EGSNR to learn more discriminative representation coefficients. Comprehensive experiments on several popular face datasets demonstrate that the proposed EGSNR outperforms the state-of-the-art regression based methods for robust face recognition.

Three-way decision provides a new perspective for dealing with uncertainty and complexity in decision-making problems. However, behaviors of decision-makers may be influenced by different risk attitudes in reality. To address this problem, in this article, we construct a regret-based three-way decision model under interval type-2 fuzzy environment. Basically, regret theory and interval type-2 fuzzy set are utilized to improve three-way decision in coping with the risk and uncertainty. Two core issues focus on the determination of decision rules and estimation of conditional probabilities for different decision-makers under interval type-2 fuzzy environment. The maximum-utility decision rules are derived based on regret theory. An interval type-2 fuzzy technique for order preference by similarity to ideal solution method is utilized to estimate the conditional probability. The results of the illustrative example show that the proposed model can effectively solve uncertain decision problems. The comparative analysis and experimental evaluations are utilized to elaborate on the performance of the regret-based three-way decision model.

Regression-based methods have been widely applied in face identification, which attempts to approximately represent a query sample as a linear combination of all training samples. Recently, a matrix regression model based on nuclear norm has been proposed and shown strong robustness to structural noises. However, it may ignore two important issues: the label information and local relationship of data. In this article, a novel robust representation method called locality-constrained discriminative matrix regression (LDMR) is proposed, which takes label information and locality structure into account. Instead of focusing on the representation coefficients, LDMR directly imposes constraints on representation components by fully considering the label information, which has a closer connection to identification process. The locality structure characterized by subspace distances is used to learn class weights, and the correct class is forced to make more contribution to representation. Furthermore, the class weights are also incorporated into a competitive constraint on the representation components, which reduces the pairwise correlations between different classes and enhances the competitive relationships among all classes. An iterative optimization algorithm is presented to solve LDMR. Experiments on several benchmark data sets demonstrate that LDMR outperforms some state-of-the-art regression-based methods.

Processing-in-Memory (PIM) or Near-Data Processing has been recognized as the most potential solution to resolve the ever-aggravating memory wall especially as the thrive of memory-intensive scale-out workloads such as graph computing and data analytics. However, when the future computing system becomes more and more likely to adopt PIM architectures as a type of the storage and processing component, there is a lack of literature and research work on the general scheduling framework with the emerging heterogeneous system except for some ad-hoc task partitioning methods with specialized PIM designs. This work is the first to propose a formalized model to quantitatively describe the multi-task scheduling problem in PIM+CPU platform without loss of generality, and also an optimized task mapping-and-scheduling algorithm to boost the hardware utility for these novel heterogeneous systems. The proposed scheduling framework is fully aware of the data access bandwidth and processing capability distinction between the CPU and PIM devices, and also the implications of task mapping on the bandwidth contention, data communication intensity and hardware utility for the concurrent workloads. Experimental results show that, compared to the traditional scheduling algorithm for heterogeneous system, the proposed method is able to improve the system performance by over 10% and the energy efficiency by almost 10% for multi-core scale-out applications.

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.

A novel dynamic intuitionistic fuzzy TOPSIS method is proposed to evaluate the agents when the expert evaluation values are Intuitionistic Fuzzy Numbers (IFNs). Similar to Positive Ideal Solution (PIS) in traditional TOPSIS, Positive Ideal Agent (PIA) is an ideal agent which achieves the best values in all the expert evaluations. Negative Ideal Agent (NIA) is the worst in every expert's evaluation. Then agent similarity to PIA and that to NIA are calculated. The relative ideal closeness of each agent is defined as a combination of the corresponding two similarities, which indicates the agent's relative capabilities. To deal with varied numbers of agents and experts, the update mechanisms are presented. A numerical example demonstrates the validity of our approach.

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.

We study simultaneous wireless information and power transfer (SWIPT) in multihop wireless cooperative networks, where the multihop capability that denotes the largest number of transmission hops is investigated. By utilizing the broadcast nature of multihop wireless networks, we first propose a cooperative forwarding power (CFP) scheme. In CFP scheme, the multiple relays and receiver have distinctly different tasks. Specifically, multiple relays close to the transmitter harvest power from the transmitter first and then cooperatively forward the power (not the information) towards the receiver. The receiver receives the information (not the power) from the transmitter first, and then it harvests the power from the relays and is taken as the transmitter of the next hop. Furthermore, for performance comparison, we suggest two schemes: cooperative forwarding information and power (CFIP) and direct receiving information and power (DFIP). Also, we construct an analysis model to investigate the multihop capabilities of CFP, CFIP, and DFIP schemes under the given targeted throughput requirement. Finally, simulation results validate the analysis model and show that the multihop capability of CFP is better than CFIP and DFIP, and for improving the multihop capabilities, it is best effective to increase the average number of relay nodes in cooperative set.

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.

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 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.

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.

This research is concerned with coordinated standoff tracking, and a guidance law against a moving target is proposed by using differential geometry. We first present the geometry between the unmanned aircraft (UA) and the target to obtain the convergent solution of standoff tracking when the speed ratio of the UA to the target is larger than one. Then, the convergent solution is used to guide the UA onto the standoff tracking geometry. We propose an improved guidance law by adding a derivative term to the relevant algorithm. To keep the phase angle difference of multiple UAs, we add a second derivative term to the relevant control law. Simulations are done to demonstrate the feasibility and performance of the proposed approach. The proposed algorithm can achieve coordinated control of multiple UAs with its simplicity and stability in terms of the standoff distance and phase angle difference.

For most attribute reduction in Pawlak rough set model (PRS), monotonicity is a basic property for the quantitative measure of an attribute set. Based on the monotonicity, a series of attribute reductions in Pawlak rough set model such as positive-region-preserved reductions and condition entropy-preserved reductions are defined and the corresponding heuristic algorithms are proposed in previous rough sets research. However, some quantitative measures of attribute set may be non-monotonic in probabilistic rough set model such as decision-theoretic rough set (DTRS), and the non-monotonic definition of the attribute reduction should be reinvestigated and the heuristic algorithm should be reconsidered. In this paper, the monotonicity of the positive region in PRS and DTRS are comparatively discussed. Theoretic analysis shows that the positive region in DTRS model may be expanded with the decrease of the attributes, which is essentially different from that in PRS model. Hereby, a new non-monotonic attribute reduction is presented for the DTRS model in this paper, and a heuristic algorithm for searching the newly defined attribute reduction is proposed, in which the positive region is allowed to be expanded instead of remaining unchanged in the process of attribute reduction. Experimental analysis is included to validate the theoretic analysis and quantify the effectiveness of the proposed attribute reduction algorithm.

With increasing demands on software functions, software systems become more and more complex. This complexity is one of the most pervasive factors affecting software development productivity. Assessing the impact of software complexity on development productivity helps to provide effective strategies for development process and project management. Previous research literatures have suggested that development productivity declines exponentially with software complexity. Borrowing insights from cognitive learning psychology and behavior theory, the relationship between software complexity and development productivity was reexamined in this paper. This research identified that the relationship partially showed a U-shaped as well as an inverted U-shaped curvilinear tendency. Furthermore, the range of complexity level that is beneficial for productivity has been presented, in which, the lower bound denotes the minimum degree of complexity at which personnel can be motivated, while the upper bound shows the maximum extent of complexity that staff can endure. Based on our findings, some guidelines for improving personnel management of software industry have also been given.

In today's business-critical environments, there is only a limited possibility that all services are to be developed from scratch. To reuse prior knowledge of existing object-oriented system design and adapt them to more flexible and scalable service-oriented systems, the paper presents a systematic approach that employs categorical models to formalize design knowledge in both kinds of software, and utilizes category theoretic computations to mechanize lifting, integration, and distribution in refactoring from object-oriented systems to service-oriented systems. Our approach provides highly abstract, modularized and effective evolution towards service-oriented computing.

A two-phase learning strategy for rule induction from incomplete data is proposed, and a new form of rules is introduced so that a user can easily identify attributes with or without missing values in a rule. Two levels of measurement are assigned to a rule. An algorithm for two-phase rule induction is presented. Instead of filling in missing attribute values before or during the process of rule induction, we divide rule induction into two phases. In the first phase, rules and partial rules are induced based on non-missing values. In the second phase, partial rules are modified and refined by the imputation of some missing values. Such rules truthfully reflect the knowledge embedded in the incomplete data. The study not only presents a new view of rule induction from incomplete data, but also provides a practical solution. Experiments validate the effectiveness of the proposed method.

Structurized video representation and indexing based on the representation are the foundation of video retrieval based on content. A novel algorithm is presented of automatically parsing and labelling video based on camera motion qualitative analysis. Firstly, the motion patterns are analyzed of the spatio-temporal slices in a video; and then the change detection and qualitative recognition of camera motion are carried out by using motion structure tensor. Finally, the results of camera motion analysis are used to parse and label video according to the camera motion coherence. The algorithm is experimentally proved effective and produces satisfactory results.