AGENT-BASED COMPUTING -- AGENT-BASED COMPUTING -- CONTENTS -- PREFACE -- AGENT-BASED GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION AND FEATURE SELECTION -- 1. INTRODUCTION -- 2. CHAIN-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION AND FEATURE SELECTION -- 2.1. Analysis of Algorithm -- 2.1.1. Chain-Like Agent Structure -- 2.1.2. Selection Process Based on Dynamic Neighboring Competition Strategy -- 2.1.3. Neighboring Crossover Process -- 2.1.4. Adaptive Mutation Process -- 2.1.5. Stop Criterion -- 2.1.6. Elitism Strategy -- 2.1.7. Realization of Algorithm -- 2.2. Experimental Results -- 2.2.1. Global Numerical Optimization Experiments -- 2.2.2. Feature Selection Experiments -- 2.3. Conclusions -- 3. MULTIPLE-POPULATION CHAIN-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION AND FEATURE SELECTION -- 3.1. Analysis of Algorithm -- 3.1.1. Multi-Population Cycle Chain-Like Agent Structure -- 3.1.2. Genetic Operators -- 3.1.3. Realization of Algorithm -- 3.1.4. Computational Complexity -- 3.2. Experimental Results -- 3.2.1. Global Numerical Optimization Experiments -- 3.2.2. Feature Selection Experiments -- 3.3. Conclusions -- CONCLUSIONS AND FUTURE WORK -- ACKNOWLEDGMENTS -- REFERENCES -- MULTI-AGENT ENTERPRISE SUSTAINABILITY PERFORMANCE MEASUREMENT SYSTEM -- ABSTRACT -- INTRODUCTION -- METHODOLOGY -- SUSTAINABILITY AGENT -- 1. The Selection of Suitable Indicators -- 2. Retrieving data from Data Repository Agent -- 3. Calculating the Weights of Indicators -- 4. Calculating Sustainability Performance Indices by Using MCDM Methods -- DATA REPOSITORY AGENT -- ALERT MANAGEMENT AGENT -- COMMUNICATION AGENT -- APPLICATION -- Sustainability Agent -- Selecting the Proper Indicators -- Retrieving the Data with Respect to the Indicators -- Calculating the Importance Weights -- Calculating the Performance Indices.

Aggregate Ranking Using Copeland method -- Calculating the Composite Sustainability Ranking Using Copeland method -- ALERT MANAGEMENT AGENT -- Communication Agent -- DISCUSSION AND IMPLICATIONS -- CONCLUSION -- APPENDIX -- REFERENCES -- A MODULAR ARTIFICIAL NEURAL NETWORK BASED DECISION MAKING IN A MULTI-AGENT ROBOT SOCCER SYSTEMS -- ABSTRACT -- 1. INTRODUCTION -- 2. THE PROBLEM DESCRIPTION -- 3. THE BASIC ANN ARCHITECTURE -- 4. MODULAR ANN ARCHITECTURE -- 5. RESULTS AND DISCUSSION -- CONCLUSION -- REFERENCES -- SECURITY AND PRIVACY IN TRACK AND TRACE INFRASTRUCTURES -- ABSTRACT -- 1. INTRODUCTION -- 1.1. Radio Frequency Identification -- 1.2. Track and Trace Infrastructures -- 2. SECURITY REQUIREMENTS -- 2.1. Confidentiality -- 2.2. Integrity -- 3. BATCH RECALLS -- 3.1. Example -- 3.2. Building Blocks -- 3.2.1. Identity-based Encryption -- 3.2.2. Boneh-Franklin Encryption -- 3.2.3. Boneh-Boyen-Goh Encryption -- 3.3. Our Solution -- 3.3.1. Solution Details for BF Encryption -- 3.3.2. Solution Details for BBG Encryption -- 3.3.3. Comparison of BF and BBG Encryption -- 3.3.3. Comparison of BF and BBG Encryption -- 4. RELATED WORK -- CONCLUSION -- REFERENCES -- A CHALLENGE TO DEVELOP LARGE-SCALE AGENT SIMULATION SOFTWARE -- ABSTRACT -- 1. INTRODUCTION -- 2. MOTIVATION -- 3. PARALLELIZATION OF MAS SOFTWARE -- 3.1. Target Simulation -- 3.2. Parallelization of MAS software on Wide-area Distributed Computing Environments -- 3.3. Performance Evaluation -- 4. ELASTIC: ENHANCED LARGE-SCALE AGENT SIMULATION TOOLKIT FOR INNOVATIVE COMMUNITY -- 4.1. Overview of ELASTIC -- 4.2. Case Study -- CONCLUSION -- ACKNOWLEDGMENTS -- REFERENCES -- A FRAMEWORK OF AN AGENT-BASED MODEL USING SOCIAL AND PHYSICAL INTERACTION FOR VULNERABILITY ANALYSIS ON FLOOD EVENTS -- ABSTRACT -- 1. INTRODUCTION -- 2. HAZARD ANALYSIS: JUDGMENT OF HAZARD AND VULNERABILITY.

3. THEORETICAL FRAMEWORK OF AGENT-BASED MODEL FOR AGENTS' INTERACTIONS -- 3.1. Jadex Engine with Knowledge Query Communication Language -- 3.2. Reinforcement Learning for Physical Interaction -- 4. IMPLEMENTATION OF AGENT-BASED MODEL WITH SOCIAL AND PHYSICAL INTERACTIONS -- 5. VULNERABILITY ANALYSIS WITH AGENT-BASED SIMULATION -- CONCLUSION -- REFERENCES -- AGENT-BASED MANUFACTURING SYSTEM INNOVATION: FRACTAL APPROACHES -- ABSTRACT -- INTRODUCTION -- FRACTAL APPROACHES IN DISTRIBUTED MANUFACTURING SYSTEMS -- Basic Concept of Fractal -- Fractal Manufacturing System (FrMS) -- Fractal Organization -- Holonic Manufacturing Systems (HMS) and Bionic Manufacturing Systems (BMS) -- SELF-EVOLUTIONARY MANUFACTURING SYSTEMS -- Employment Network (Emnet) -- Constituent Entity -- FUZZY GOAL MODEL -- Fuzzy Set Theory -- Embodiment Framework -- Evaluating Agents: Trust Aspect -- Fuzzy Inference Engine -- IMPLEMENTATION AND EXPERIMENTAL ANALYSIS -- Prototype Implementation: A Test-Bed -- Experimental Setup -- Analysis of Simulation Results -- CONCLUSION -- ACKNOWLEDGMENT -- REFERENCES -- AGENT-BASED DISCOVERY, COMPOSITION AND ORCHESTRATION OF GRID WEB SERVICES -- ABSTRACT -- 1. INTRODUCTION -- 2. AGENTS, WEB SERVICES, ONTOLOGIES AND THE SERVICE GRID -- 2.1. Web Services -- 2.2. Service Composition -- 2.3. Ontologies and Semantic Web Services -- 2.4. Grid Computing -- 2.5. Agents for Service Discovery, Composition and Orchestration -- 3. A SERVICE GRID ARCHITECTURE -- 4. A PROPOSED IMPLEMENTATION FRAMEWORK -- 4.1. General Description -- 4.2. Server Side Architecture -- 4.3. Interaction with the Multi-Agent System -- 4.4. The Service Alignment Tool -- 5. A CASE STUDY -- 5.1. The Developed Ontology -- 5.2. Supported Content and Services -- 5.3. An Example of Use -- SUMMARY AND CONCLUSIONS -- REFERENCES.

AN EFFICIENT MOBILE-AGENT-BASED PLATFORM FOR DYNAMIC SERVICE PROVISIONING IN 3G/UMTS -- ABSTRACT -- I. INTRODUCTION -- II. EXISTING APPROACHES -- III. PRELIMINARY -- A. Introducing CAMEL Concept -- B. UMTS CAMEL Architecture Integrated with Mobile Agent Technology -- IV. PROPOSED MOBILE AGENT-BASED PLATFORM FOR DYNAMIC SERVICE PROVISIONING IN UMTS CAMEL ARCHITECTURE -- A. Agent-based CAMEL Call Processing in UMSC -- B. Registration and Location Update -- C. Dynamic Service Provision -- V. ANALYSIS AND DISCUSSION -- CONCLUSIONS -- REFERENCES -- ANINVESTIGATIONINTOTHEISSUESOFMULTI-AGENTDATAMINING -- Abstract -- 1Introduction -- 1.1Motivation -- 1.2Objectives -- 1.3Evaluation -- 1.4Extendibility -- 1.5OverviewofEMADSImplementedScenarios -- 1.5.1MetaAssociationRuleMining(ARM) -- 1.5.2VerticalPartitioningandDistributed/ParallelARM -- 1.5.3GenerationofClassifiers -- 1.6ChapterOrganization -- 2BackgroundandLiteratureReview -- 2.1DataMining -- 2.1.1AssociationRuleMining -- 2.1.2TheAprioriAlgorithm -- 2.1.3ClassificationRuleMining -- 2.1.4ClassificationbyDecisionTrees -- 2.2DistributedDataMining -- 2.3AgentsandMulti-AgentSystems -- 2.3.1Agents -- 2.3.2Multi-AgentSystems -- 2.3.3MASDevelopmentPlatforms -- 2.4Multi-AgentDataMining -- 2.4.1Central-learningStrategy -- 2.4.2Meta-learningStrategy -- 2.4.3Hybrid-learningStrategy -- 2.5Summary -- 3EMADS:RequirementsAnalysis,Design,andImplementation -- 3.1RequirementsAnalysis -- 3.2StructuralRequirements -- 3.3OperationalRequirements -- 3.4EMADSAgentsandUsers -- 3.4.1UserAgent -- 3.4.2TaskAgent -- 3.4.3FacilitatorAgent(orBrokerAgent) -- 3.4.4DataMining(DM)Agent -- 3.4.5DataAgent(orResourceAgent) -- 3.4.6EMADSEndUserCategories -- 3.5DefiningInteractionProtocols -- 3.5.1FindOtherAgentsProtocol -- 3.5.2AgentRegistrationProtocol -- 3.5.3UserDMRequestProtocol -- 3.5.4StartingDataMiningProtocol.

3.5.5AdvertiseAgentCapabilitiesProtocol -- 3.5.6PerformDataMiningProtocol -- 3.5.7DataRetrievalProtocol -- 3.6DataMiningwithEMADSAgents -- 3.6.1DMTaskPlanningandFlowofSystemOperationsforOnePossibleScenario -- 3.7TheAgentDevelopmentToolkit -- 3.7.1JADE -- 3.7.2JADEAgentInteraction -- 3.8EMADSArchitectureasImplementedinJade -- 3.8.1MappingEMADSProtocolstoJADEBehaviours -- 3.8.2AgentInteractions -- 3.8.3MechanismsofCooperation -- 3.8.4UserRequestHandling -- 3.9Summary -- 4EMADSExtendibility -- 4.1Wrappers -- 4.1.1DataWrappers -- 4.1.2DMWrappers -- 4.1.3TaskWrappers -- 4.1.4Discretization/Normalization -- 4.2Implementation -- 4.2.1TaskAgents -- 4.2.2DataAgents -- 4.2.3DMAgents -- 4.3Summary -- 5FrequentSetMetaMining:MetaARM -- 5.1BackgroundandPreviousWork -- 5.2NoteonPandTTrees -- 5.3ProposedMetaARMAlgorithms -- 5.3.1BenchMarkAlgo -- 5.3.2BruteForceMetaARMAlgorithm -- 5.3.3AprioriMetaARMAlgorithm -- 5.3.4HybridMetaARMAlgorithm1and2 -- 5.4MetaARMEMADSMode -- 5.4.1DynamicBehaviourofEMADSforMetaARMoperations -- 5.5Noteondatasets -- 5.5.1ExperimentationandAnalysis -- 5.6Summary -- 6VerticalPartitioningandDistributed/ParallelARM -- 6.1BackgroundandPreviousWork -- 6.2TheT-treeandtheApriori-Talgorithm -- 6.3TheDistributedandParallelTaskwithVerticalPartitioning(DATA-VP)Algorithm -- 6.4Architectureandnetworkconfiguration -- 6.4.1Messaging -- 6.5ExperimentationandAnalysis -- 6.6Summary -- 7ClassifierGeneration -- 7.1Background -- 7.2EMADSOperation:ClassifierGeneration -- 7.3ExperimentationandAnalysis -- 7.4Summary -- 8Conclusion -- References -- INDEX.