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On discussing the necessary features for a group of agents to argue about their positions and intentions over a specific issue some questions arise: Why do agents interact? Is saving the knowledge generated in the interaction useful? How do agents manage arguments? How do agents dialogue? CBR is an adequate way to tackle such argumentation issues in MAS. This paper clarifies the advances achieved by applying CBR to argumentation in MAS, identifies open issues and proposes new ideas to face future challenges. [ABSTRACT FROM AUTHOR]

Organizational Theory concepts have been adapted to improve, extend and facilitate Agent Organizations modeling. Thus, three basic organizational topologies from which other more complex organizations can be composed have been identified. Moreover, this organizational modeling has been implemented in a specific MAS platform (SPADE) making use of Multiuser Conference technology. We have also defined several services for SPADE that ease the implementation of organization dynamics, such as member access and agent interaction control. [ABSTRACT FROM AUTHOR]

This paper defines a meta-reasoning model for real-time agents. The purpose of this model is to increase the adaptability in this kind of agents. This model allows to adapt the agent's behavior as well as the agent's own reasoning process. The application of this model to an specific real-time agent architecture is also presented. [ABSTRACT FROM AUTHOR]

A novel shape descriptor, chord length function (CLF) which can be obtained by equal arc length partitions of a contour, is proposed. The difference of two shapes is measured by the distance between their corresponding CLF. The proposed CLF is invariant to rotation, scaling and translation. It is robust to noise and simple to compute. Experimental results indicate that CLF is an effective shape descriptor. [ABSTRACT FROM AUTHOR]

The vision system of human beings is very sensitive to lines. Sketches composed of line drawings provide a useful representation for image structures, which can be used for recognition and which is capable of serving as intermediate data structures of images for further analysis. However, traditional schemes tackling the line detection problem for sketch generation are stuck to one of the two problems: lack of global supervision for line detection over an image or confinement of hard global constraints on the configuration of detected lines.In this paper, we propose an active sketch model for finding primary structures in the form of line drawings, overcoming shortcomings of the traditional schemes by combining local line finding techniques with supervision of a global saliency measure while imposing no global constraint on the configuration of the line drawings.Test results show that our model is able to sketch out lines in an image representing the most salient image discontinuity without imposing any shape or topology constraint on the sketch. [ABSTRACT FROM AUTHOR]

The uncertain regions are modeled by broad boundary regions, while the description and reasoning of combined spatial relations between them are still unresolved problems. So far, the research about describing spatial relations mainly focuses on single-kind of spatial relation between simple and certain objects, rather than the combination of multi-kinds of spatial relations between uncertain regions. In addition, the uncertainty of formalizing combined spatial relations between uncertain regions is still a puzzle problem. Based on this point, the rough set theory is introduced into describing combined spatial relations between broad boundary regions. First, topological relations, exterior direction relations and detailed direction relations are approximated by a lower and an upper rough set, respectively, and then the difference set between the two sets indicates the uncertainty of spatial relations. Finally, the combination of the topological and direction relations between broad boundary regions is used to describe combined spatial relations. Keywords: Broad boundary region, direction relation, topological relation. [ABSTRACT FROM AUTHOR]

Statistical re-sampling techniques have been used extensively and successfully in the machine learning approaches for generations of classifier and predictor ensembles. It has been frequently shown that combining so called unstable predictors has a stabilizing effect on and improves the performance of the prediction system generated in this way. In this paper we use the re-sampling techniques in the context of a topology preserving map which can be used for scale invariant classification, taking into account the fact that it models the residual after feedback with a family of distributions and finds filters which make the residuals most likely under this model. This model is applied to artificial data sets and compared with a similar version based on the Self Organising Map (SOM). [ABSTRACT FROM AUTHOR]

Unbalanced data in a classification problem appears when there are many more instances of some classes than others. Several solutions were proposed to solve this problem at data level by under-sampling. The aim of this work is to propose evolutionary prototype selection algorithms that tackle the problem of unbalanced data by using a new fitness function. The results obtained show that a balancing of data performed by evolutionary under-sampling outperforms previously proposed under-sampling methods in classification accuracy, obtaining reduced subsets and getting a good balance on data. [ABSTRACT FROM AUTHOR]

Large repositories of spatial data have been formed in various applications such as Geographic Information Systems (GIS), environmental studies, banking, etc. The increasing demand for knowledge residing inside these databases has attracted much attention to the field of Spatial Data Mining. Due to the common complexity and huge size of spatial databases the aspect of efficiency is of the main concerns in spatial knowledge discovery algorithms. In this paper, we introduce two novel nature-inspired algorithms for efficient discovery of spatial trends, as one of the most valuable patterns in spatial databases. The algorithms are developed using ant colony optimization and evolutionary search. We empirically study and compare the efficiency of the proposed algorithms on a real banking spatial database. The experimental results clearly confirm the improvement in performance and effectiveness of the discovery process compared to the previously proposed methods. [ABSTRACT FROM AUTHOR]

MOVICAB-IDS enables the more interesting projections of a massive traffic data set to be analysed, thereby providing an overview of any possible anomalous situations taking place on a computer network. This IDS responds to the challenges presented by traffic volume and diversity. It is a connectionist agent-based model extended by means of a functional and mobile visualization interface. The IDS is designed to be more flexible, accessible and portable by running on a great variety of applications, including small mobile ones such as PDA's, mobile phones or embedded devices. Furthermore, its effectiveness has been demonstrated in different tests. Keywords: Unsupervised Learning, Neural Networks, Exploratory Projection Pursuit, Multiagent Systems, Computer Network Security, Intrusion Detection. [ABSTRACT FROM AUTHOR]

This paper presents new application of Particle Swarm Optimization (PSO) algorithm for training Hidden Markov Models (HMMs). The problem of finding an optimal set of model parameters is numerical optimization problem constrained by stochastic character of HMM parameters. Constraint handling is carried out using three different ways and the results are compared to Baum-Welch algorithm (BW), commonly used for HMM training. The global searching PSO method is much less sensitive to local extremes and finds better solutions than the local BW algorithm, which often converges to local optima. The advantage of PSO approach was markedly evident, when longer training sequence was used. [ABSTRACT FROM AUTHOR]

Biological processes and methods have been influencing science and technology for many decades. The ideas of feedback and control processes Norbert Wiener used in his cybernetics were based on observation of these phenomena in biological systems. Artificial intelligence and intelligent systems have been fundamentally interested in the phenomenology of living systems, namely perception, decision-making, action, and learning. Natural systems exhibit many properties that form fundamentals for a number of nature inspired applications - dynamics, flexibility, robustness, self-organisation, simplicity of basic elements, and decentralization. This paper reviews examples of nature inspired software applications focused on optimization problems, mostly drawing inspiration from collective behaviour of social colonies. [ABSTRACT FROM AUTHOR]

Many applications require ordering of instances represented by high dimensional vectors. Despite the reasonable quantity of papers on classification and clustering, papers on multidimensional ranking are rare. This paper expands a generic ranking procedure based on one-dimensional self-organizing maps (SOMs). The typical similarity metric is modified to a weighted Euclidean metric and automatically adjusted by a genetic search. The search goal is the best ranking that matches the desired probability distribution (provided by experts) leading to a context-sensitive metric. To ease expert agreement the technique relies on consensus about the best and worst instances. Besides the ranking task, the derived metric is also useful on reducing the number of dimensions (questionnaire items in some situations) and on modeling the data source. Promising results were achieved on the ranking of data from blood bank inspections and client segmentation in agribusiness. [ABSTRACT FROM AUTHOR]

Our work is within the framework of studying and implementing a sound analysis system in a telemedicine project. The task of this system is to detect situations of distress in a patient's room based sound analysis. In this paper we present our works on building domain ontologies of such situations. They gather abstract concepts of sounds and these concepts, along with their properties and instances, are represented by a neural network. The ontology-based classifer uses outputs of networks to identify classes of audio scenes. The system is tested with a database extracted from films. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms has been recently used for prototype selection showing good results. An important problem in prototype selection consist in increasing the size of data sets. This problem can be harmful in evolutionary algorithms by deteriorating the convergence and increasing the time complexity. In this paper, we offer a preliminary proposal to solve these drawbacks. We propose an evolutionary algorithm that incorporates knowledge about the prototype selection problem. This study includes a comparison between our proposal and other evolutionary and non-evolutionary prototype selection algorithms. The results show that incorporating knowledge improves the performance of evolutionary algorithms and considerably reduces time execution. [ABSTRACT FROM AUTHOR]

D-S evidence theory has been studied and used for information fusion for a while. Though D-S evidence theory can deal with uncertainty reasoning from imprecise and uncertain information by combining cumulative evidences for changing prior opinions using new evidences. False evidence generated by any fault sensor will result in evidence conflict increasing and inaccurate fused results. Evidence relationship matrix proposed in this paper depicts the relationship among evidences. False evidences can be identified through the analysis of relationships among evidences. Basic probability assignments related to the false evidences may be decreased accordingly. The accuracy of information fusion may be improved. Case studies show the effectiveness of the proposed method. Keywords: evidence relationship matrix, D-S evidence theory, and information fusion. [ABSTRACT FROM AUTHOR]

Since the outbreak of connectionist modelling in the mid eighties, several problems in natural language processing have been tackled by employing neural network-based techniques. Neural network's "biological plausibility" offers a promising framework in which the computational treatment of language may be linked to other disciplines such as cognitive science and psychology. With this brief survey, we set out to explore the landscape of artificial neural models for the acquisition of language that have been proposed in the research literature. [ABSTRACT FROM AUTHOR]

A data mining procedure for automatic determination of fuzzy decision tree structure using a genetic program is discussed. A genetic program (GP) is an algorithm that evolves other algorithms or mathematical expressions. Methods for accelerating convergence of the data mining procedure are examined. The methods include introducing fuzzy rules into the GP and a new innovation based on computer algebra. Experimental results related to using computer algebra are given. Comparisons between trees created using a genetic program and those constructed solely by interviewing experts are made. Connections to past GP based data mining procedures for evolving fuzzy decision trees are established. Finally, experimental methods that have been used to validate the data mining algorithm are discussed. Keywords: Genetic Programs, Fuzzy Logic, Data Mining, Control Algorithms, Planning Algorithms. [ABSTRACT FROM AUTHOR]

The aim of this research is to use and compare clustering technologies and find the best method for establishing theoretical SAPK/JNK signaling pathway in human soft tissue sarcoma samples. Centroid linkage, Single linkage, complete linkage and average linkage hierarchical clustering are used to arrange genes for setup signaling pathways according to similarity in pattern of gene. The results show that centriod linkage, complete linkage, and average linking clustering architecture is consistent with the core unit of the cascade composed of a CDC42, a MEKK1 (map3k1), a MKK4 (map2k4), a JNK1(mapk8) to a ATF2 in hierarchical clustering. An activated Jnk phosphorylates a variety of transcription factors regulating gene expression, such as ATF2. This study implies that centroid linkage, complete linkage and average linkage clustering method in uncentered (absolute) correlation similarity measures fits for establishing theoretical SAPK/Jnk signaling pathway in human soft tissue sarcoma samples which is consistent with biological experimental SAPK/Jnk signaling pathway [ABSTRACT FROM AUTHOR]

We propose an enhanced QA model with combination of hetero-geneous answer acquisition methods. Our QA system is based on web encyclopedia in Korean. We investigated characteristic features of the encyclopedia and incorporate them in our answer acquisition methods. We defined three different types of answer extraction methods: learning-based, pattern-based, and traditional statistical methods. By empirical experiments, we obtained 59% improvement on MRR as well as 2.3 times speedy response. Keywords: Question Answering, Knowledge acquisition, Machine learning. [ABSTRACT FROM AUTHOR]

In this paper we propose some methods to build a kernel matrix for classification purposes using Support Vector Machines (SVMs) by fusing polynomial kernels. The proposed techniques have been successfully evaluated on artificial and real data sets. The new methods outperform the best individual kernel under consideration and they can be used as an alternative to the parameter selection problem in polynomial kernel methods. [ABSTRACT FROM AUTHOR]

We propose a classification method based on a special class of feed-forward neural network, namely product-unit neural networks. They are based on multiplicative nodes instead of additive ones, where the nonlinear basis functions express the possible strong interactions between variables. We apply an evolutionary algorithm to determine the basic structure of the product-unit model and to estimate the coefficients of the model. We use softmax transformation as the decision rule and the cross-entropy error function because of its probabilistic interpretation. The empirical results over four benchmark data sets show that the proposed model is very promising in terms of classification accuracy and the complexity of the classifier, yielding a state-of-the-art performance. Keywords: Classification; Product-Unit; Evolutionary Neural Networks. [ABSTRACT FROM AUTHOR]

Random Walks (RW) search technique can greatly reduce bandwidth production but generally fails to adapt to different workloads and environments. A Random Walker can't learn anything from its previous successes or failures, displaying low success rates and high latency. In this paper, we propose Intelligent Walks (IW) search mechanism - a modification of RW, exploiting the learning ability and the shortest path distance of node neighbors. A node probes its neighbors before forwarding the query. The probe is to find a candidate that has the shortest distance from the query source and/or has ever seen before the object that is going to be sent. If there isn't such candidate, then a node is chosen as usual (at random). The experimental results demonstrate that new method achieves better performance than RW in terms of success rate. Keywords: Unstructured P2P, Search, Random Walks and Intelligent Walks. [ABSTRACT FROM AUTHOR]

The fuzzy approach to regression has been traditionally considered as a problem of linear programming. In this work, we introduce a variety of models founded on quadratic programming together with a set of indices useful to check the quality of the obtained results. In order to test the validness of our proposal, we have done an empirical study and we have applied the models in a case with financial data: the Chilean COPEC Company stock price. [ABSTRACT FROM AUTHOR]

In this paper we describe how an evidential-reasoner can be used as a component of risk assessment of engineering projects using a direct way of reasoning. Guan & Bell (1991) introduced this method by using the mass functions to express rule strengths. Mass functions are also used to express data strengths. The data and rule strengths are combined to get a mass distribution for each rule; i.e., the first half of our reasoning process. Then we combine the prior mass and the evidence from the different rules; i.e., the second half of the reasoning process. Finally, belief intervals are calculated to help in identifying the risks. We apply our evidential-reasoner on an engineering project and the results demonstrate the feasibility and applicability of this system in this environment. Keywords: Evidential reasoning, Risk assessment, Dempster-Shafer theory, Knowledge-based, Uncertainty. [ABSTRACT FROM AUTHOR]

The problems related to the investment selection and risk management are very important for the insurance corporation and are considered in this paper. When performing the investment selection and risk management, the insurance corporation should suitably choose the policy among a variety of production/business policies with different risk and profit potential. In this paper, the optimal investment selection and risk management policy for the insurance corporation is defined as the objective to find the policy which maximizes the expected total discounted dividend pay-out until the time of bankruptcy. Keywords: Investment selection, Risk Management, Insurance Corporation. [ABSTRACT FROM AUTHOR]

This paper analysis dynamics of JPY/USD and USD/EUR currency option RND (Risk-Neutral Density) function and moments during CNY realignment. Empirical study shows CNY realignment only has limited impact on USD expectation. The influence of CNY on FX market should not be exaggerated. Keywords: CNY realignment, USD expectation, Currency option, RND function. [ABSTRACT FROM AUTHOR]

This paper proposes markovian models in portfolio theory and risk management. In a first analysis, we describe discrete time optimal allocation models. Then, we examine the investor's optimal choices either when returns are uniquely determined by their mean and variance or when they are modeled by a Markov chain. Moreover we propose different models to compute VaR and CVaR when returns are modeled by a Markov chain. [ABSTRACT FROM AUTHOR]

There are a great number of programming languages and frameworks for multi-agent system development, but most of them do not allows us create and later manage real-time multi-agent systems. In this paper, we propose the use of the RT-Java language for real-time multi-agent development. We also present the jART platform as an appropriate framework for the execution of agents of this kind. A mobile robot control is implemented and analyzed using this framework. [ABSTRACT FROM AUTHOR]

In this paper, first, our J-IFC-BRIDGE product model, which is a combination of YLPC-BRIDGE and YLSG-BRIDGE, and French IFC-BRIDGE product models were described. Then, the process of merging these models into the New IFC-BRIDGE was presented, and New IFC-BRIDGE was introduced. Further, the concrete bridge design system using multi-agents that had been developed before was modified to follow the changes of the product model, and the new system and the product model were verified by applying the design scenario of a prestressed concrete girder. Both the New IFC-BRIDGE and the concrete bridge design system showed the feasibility and practicality. Keywords: product data model, IFC, bridge, design checking, multi-agents. [ABSTRACT FROM AUTHOR]

In the last years, much work have been done in the development of techniques for automated negotiation, and, particularly, in the automated negotiation of SLAs. However, there is no work that describes how to develop advanced software systems that are able to negotiate automatically in an open environment such as the Internet. In this work, we develop a conceptual framework for automated negotiations of SLAs that serves as a starting point by identifying the elements that must be supported in those software systems. In addition, based on that conceptual framework, we report on a set of properties for automated negotiation systems that may be used to compare different proposals. [ABSTRACT FROM AUTHOR]

The idea that people use the Web and make the Web at the same time is an interesting starting point to study it. Personal homepages, blogs and similar websites can be studied as a social network phenomenon because social characteristics can explain their nature and dynamic. We present a computational Agent-Based model of personal web communities. Agents maintain their homepages and the web network emerges as they make links to colleagues' homepages, with whom they share common interests. Three simple rules we have summarized in the "similarity", "bookmarks" and "activity" concepts allow to explain most of the network properties presented in real web communities. The similarity in personal interests conditions the network structure, mainly the average social distance between individuals. The way in which agents search information in the Web influences the probability of meeting new people and has a big effect on the transitivity of the web network. Keywords: blogs, personal homepages, Social Networks, Agent-Based Modeling. [ABSTRACT FROM AUTHOR]

Recent development of teachable agent focuses on individualization and provides learners with active roles of knowledge constructors. The adaptive agent aims to maximize the learner's cognitive functions as well as to enhance the learner's interests in and motivation for learning. To establish the relationships between user characteristics and response patterns and to develop an algorithm based on the relationship, the individual characteristics of the learner were measured and the log data during interaction with the teachable agent named KORI were collected. A correlation analysis was conducted to identify the relationships among individual characteristics, user responses, and learning. Of the hundreds of possible relationships among numerous variables in three dimensions, nine key user responses were extracted, which were highly correlated with either the individual characteristics or learning outcomes. The results suggested that the construction of an individualized student model based on the ongoing response pattern of the user would be useful indices for predicting the learners' individual characteristics and ongoing learning outcome. This study proposes a new type of method for assessing individual differences and dynamic cognitive/motivational changes rather than measuring them directly before or after learning. Keywords: Adaptive agent, Teachable agent, Student modeling, Motivation, Interest, Comprehension, Individualization. [ABSTRACT FROM AUTHOR]

Internet auctions in open dynamic environments have been attracting increasing attention. We analyze with a bottom-up approach the competition between artificial intelligent agents in Continuous Double Auction markets. In almost all previous works agents have a fixed bidding strategy during the auction, usually under static symmetric environments. In our simulations we allow the soft-agents to learn not only about how much they should bid or ask, but also about possible switching between the alternative strategies. We examine the behaviour of strategic traders under dynamic asymmetric environments thus extending previous results. This analysis is important in the design and performance of auctions in the real world (stock exchanges, commodity markets, emission permits, and B2B exchanges) and in the applications of auction theory to many problems in management and production, far beyond market design (market oriented programming). [ABSTRACT FROM AUTHOR]

Context-aware adaptation service is rapidly becoming an important issue. This service overcomes the limitations of wireless devices and maintains adequate service levels in changing environments. The majority of existing studies concentrate on adaptation modules on the client, proxy, or server. These existing studies thus suffer from the problem of having the workload concentrated on a single system. This means that increasing the number of users increases the response time of a user's request. In this paper, the adaptation module is dispersed and arranged over the client, proxy, and server. The module monitors the context of each system and creates a dispersed adaptation system, optimized for efficient operation. Through this method, faster adaptation work is made possible, even when the number of users increases, and the dividing workload makes more stable system operation possible. In order to evaluate the proposed system, a prototype is constructed and dispersed operations are tested using multimedia content, simulating server overload and comparing the response times and system stability with existing server based adaptation systems. The effectiveness of the system is confirmed through the results. [ABSTRACT FROM AUTHOR]

Testing interactions in multi-agent systems is a complex task because of several reasons. Agents are distributed and can move through different nodes in a network, so their interactions can occur concurrently and from many different sites. Also, agents are autonomous entities with a variety of possible behaviours, which can evolve during their lives by adapting to changes in the environment and new interaction patterns. Furthermore, the number of agents can vary during system execution, from a few dozens to thousands or more. Therefore, the number of interactions can be huge and it is difficult to follow up their occurrence and relationships. In order to solve these issues we propose the use of a set of data mining tools, the ACLAnalyser, which processes the results of the execution of large scale multi-agent systems in a monitored environment. This has been integrated with an agent development toolset, the INGENIAS Development Kit, in order to facilitate the verification of multi-agent system models at the design level rather than at the programming level. [ABSTRACT FROM AUTHOR]

The growing interest in mobile devices in new different scenarios increases the need to create context-aware multi-agent systems. Interaction between users is produced in many environments and new mobility technologies allow access information anytime and anywhere. Many researches and location systems have focused in specific domains to share knowledge between agents. This article proposes a global ontology to let agents work with heterogeneous domains using a wireless network. The intention of the proposed ontology is to provide customization about different environment services based on user location and profile. Keywords: ontology, heterogeneous domain, multi-agent system, wireless network. [ABSTRACT FROM AUTHOR]

Multi-agent systems defined on a network can be used for modelling the competition between companies in terms of market dominance. In view of the enormous size of the search space for winning strategies of initial configuration of resource allocation on network, we focus our search on the subspace defined by special local clustering effects, using the recently developed evolutionary computational algorithm. Strategies that emphasize local solidarity, measured by the formation of clusters in the form of triangles linkage between members of the same company, prove to be effective in winning both the market share with high probability and high speed. The result provides a good guideline to improve the collective competitiveness in a network of agents. The formulation is based on the Ising model in statistical physics and the evolutionary game is based on Monte Carlo simulation. Significance and the application of the algorithm in the context of econophysics and damage spreading in network are discussed. [ABSTRACT FROM AUTHOR]

In an open and dynamic environment, an organization has to cope with various changes from both the external environment and the internal elements in multi-agent systems. Reorganization is the essential capability for an organization to achieve organizational objectives flexibly and becomes one of the key issues of organization theories. To represent formally the dynamic process of reorganization in multi-agent systems, in this paper, we formulate a graph transformation based model of reorganization process in the context of organizational structures. In this model, a multi-level graph is proposed to capture main elements and their inter-relations of the organization and the specific operational semantics of organizational changes are defined with the graph manipulation of this graph based on graph transformation rules. Finally, for methodologies, we specify the examples of applying this graph transformation approach to the transition of organizational structures during the reorganization process. Keywords: Dynamic reorganization; multi-level graph; graph transformation, multi-agent system. [ABSTRACT FROM AUTHOR]

Peer-to-peer computing has been emerged as very popular application due to the strong retrieval performance and the easiness of sharing resource and information. Nonetheless in reality p2p users are demanding more privacy rather than share of information. In this study we propose mutual anonymity technique based on length-restricted multi-path for identity of initiator and responder and the anonymous communication between initiator and responder. In this technique is not easily revealed to denial of service attack by ensuring the anonymity of initiator and responder and anonymous communication is possible by means of grouping technique of intermediate nodes. Compared with the previous studies privacy can easily be ensured without causing cryptography processing overhead by using former protocol only. Overhead, expected in the proposed technique of intermediate node grouping, is evaluated through test. [ABSTRACT FROM AUTHOR]

This paper presents a multiagent model that facilitates aspects of shopping mall management, as well as increasing the quality of leisure facilities and shopping on offer. The work presented focuses on the use of a multi agent architecture, based on the use of deliberative agents that incorporates case-based planning. The architecture considers a dynamic framework, and the need to use autonomous models that are able to evolve over time. The architecture incorporates agents whose aim is to acquire knowledge and adapt themselves to the environmental changes. The system has been tested successfully, and the results obtained are presented in this paper. Keywords: CBR, CBP-BDI, wireless multiagent system, shopping mall, SMAS. [ABSTRACT FROM AUTHOR]

Multiple teleoperated robots are more powerful to accomplish complex tasks than single robot does, and coordinated control of multiple robots play an important role in improving their performances. A knowledge based intelligent control method is proposed to coordinate the autonomous actions of the two teleoperated robots. In this method, each intelligent agent of the teleoperated robots has a model of other robots and a knowledge base of the task in a virtual distributed graphic predictive display system. Based on minimum distance query and collision detection, the autonomous motion of each robot is then planned via knowledge based reasoning to accomplish the given task. Experimental simulation results show that the method is effective for multiple robots to work coordinately. Keywords: Multiple robots, Virtual reality, Intelligent control. [ABSTRACT FROM AUTHOR]