Intro -- AGENT-BASED COMPUTING -- AGENT-BASED COMPUTING -- CONTENTS -- PREFACE -- Chapter 1 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 -- a) Discussion on Selection Process with Real Coding -- b) Discussion on Selection Process with Binary Coding -- 2.1.3. Neighboring Crossover Process -- a) Neighboring orthogonal Crossover Operator with Real Coding -- b) Adaptive Neighboring Crossover Operator with Binary Coding -- 2.1.4. Adaptive Mutation Process -- a) Adaptive Mutation Operator with Real Coding -- b) Adaptive Mutation Operator with Binary Coding -- 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 -- a) Tested Functions -- b) Optimization Results and Analysis -- 2.2.2. Feature Selection Experiments -- a) Tested Databases and Experimental Conditions -- b) Comparison of Feature Selection Capability -- b-1). Comparison of CAGA and SGAE -- b-2) Comparison of CAGA and AGA -- b-3). Comparison of CAGA and SFGA -- c) Classification Experiments on the Feature Subset from Four Genetic Algorithms -- c-1). Comparison of Classification Results Based on Database 1 -- c-2). Comparison of Classification Results Based on Database 2 -- 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 -- a) Low Dimensional Optimization Experiments for MPAGA and MAGA -- b) Middle and high dimensional optimization experiments for MPAGA and MAGA -- c) Analysis of Convergence Performance -- d) The study of the Number of Shared Agents and Size of Sub-Populations -- 3.2.2. Feature Selection Experiments -- a) Feature Selection Experiments with Filter Methods -- b) Feature Selection Experiments with Wrapper Methods -- c) Feature Selection Experiments with Different Classifiers -- d) The study of the Number of Shared Agents and Size of Subpopulations -- e) Comparison with Parallel Feature Selection Method -- 3.3. Conclusions -- CONCLUSIONS AND FUTURE WORK -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 2 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 -- Chapter 3 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 -- Chapter 4 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 -- 3.3.4. Integration into Business Applications -- 4. RELATED WORK -- CONCLUSION -- REFERENCES -- Chapter 5 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 -- Chapter 6 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. Chapter 7 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 -- Organizing Events -- Example -- Fractal-Based Virtual Enterprise Model -- FUZZY GOAL MODEL -- Fuzzy Set Theory -- Embodiment Framework -- Evaluating Agents: Trust Aspect -- Fuzzy Inference Engine -- IMPLEMENTATION AND EXPERIMENTAL ANALYSIS -- Prototype Implementation: A Test-Bed -- Experiment 1: Emnet Formation -- Experiment 2: Goal Regulation -- Experimental Setup -- Fuzzy Regulation Rules -- Resource Configuration and Exemplary Tasks -- Analysis of Simulation Results -- CONCLUSION -- ACKNOWLEDGMENT -- REFERENCES -- Chapter 8 AGENT-BASED DISCOVERY, COMPOSITION AND ORCHESTRATION OF GRID WEB SERVICESā -- 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 -- 4.3. 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 -- Chapter 9 AN EFFICIENT MOBILE-AGENT-BASED PLATFORM FOR DYNAMIC SERVICE PROVISIONING IN 3G/UMTS1 -- ABSTRACT -- I. INTRODUCTION -- II. EXISTING APPROACHES -- III. PRELIMINARY. A. Introducing CAMEL Concept -- B. UMTS CAMEL Architecture Integrated with Mobile Agent Technology -- 1) Integration of an Agent System into CAMEL -- 2) Agent Entities in UMTS CAMEL Architecture -- 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 -- 1) Incoming Calls Scenarios -- 2) Outgoing Calls Scenarios -- V. ANALYSIS AND DISCUSSION -- 1) Incoming Call Analysis -- 2) Outgoing Call Analysis -- CONCLUSIONS -- REFERENCES -- Chapter10ANINVESTIGATIONINTOTHEISSUESOFMULTI-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.
9781611225761
Intelligent agents (Computer software). Distributed artificial intelligence. Data mining.