Contents. Dedication List of Figures List of Tables. Acknowledgments

Transcription

1 Contents Dedication List of Figures List of Tables Foreword Preface Acknowledgments v xiii xvii xix xxi xxv Part I Concepts and Techniques 1. INTRODUCTION 3 1 The Quest for Knowledge 3 2 Problem Description 4 3 Related Bibliography 5 4 Scope of the Book 6 5 Contents of the Book 8 6 How to Read this Book 9 2. DATA MINING AND KNOWLEDGE DISCOVERY: A BRIEF OVERVIEW 11 1 History and Motivation The Emergence of Data Mining So, what is Data Mining? The KDD Process Organizing Data Mining Techniques 15 2 Data Preprocessing The Scope of Data Preprocessing Data Cleaning 18

2 viii A GENT INTELLIGENCE THR 0 UGH DA TA MINING 2.3 Data Integration Data Transformation Data Reduction Data Discretization 20 3 Classification and Prediction Defining Classification Bayesian Classification Decision Trees The ID3 algorithm 24 4 Clustering Definitions Clustering Techniques Representative Clustering Algorithms Partitioning Algorithms Hierarchical Algorithms Density-Based Algorithms 30 5 Association Rule Extraction Definitions Representative Algorithms 33 6 Evolutionary Data Mining Algorithms The Basic Concepts of Genetic Algorithms Genetic Algorithm Terminology Genetic Algorithm Operands The Genetic Algorithm Mechanism Application of Genetic Algorithms 38 7 Chapter review INTELLIGENT AGENTS AND MULTI-AGENT SYSTEMS 41 1 Intelligent Agents Agent Definition Agent Features and Working Definitions Agent Classification Agents and Objects Agents and Expert Systems Agent Programming Languages 47 2 Multi-Agent Systems Multi-Agent System Characteristics Agent Communication Agent Communication Languages 53

3 Contents KQML KIF FIPA ACL Agent Communities ix Part II Methodology 4. EXPLOITING DATA MINING ON MAS 59 1 Introduction Logic and Limitations Agent Training and Knowledge Diffusion Three Levels of Knowledge Diffusion for MAS 63 2 MAS Development Tools 63 3 Agent Academy A A Architecture Developing Multi-Agent Applications Creating Agent Ontologies Creating Behavior Types Creating Agent Types Deploying a Multi Agent System COUPLING DATA MINING WITH INTELLIGENT AGENTS 71 1 The Unified Methodology Formal Model Case 1: Training at the MAS application level Case 2: Training at the MAS behavior level Case 3: Training evolutionary agent communities Common Primitives for MAS Development Application Level: The Training Framework Behavior Level: The Training Framework Evolutionary Level: The Training Framework 80 2 Data Miner: A Tool for Training and Retraining Agents Prerequisites for Using the Data Miner Data Miner Overview Selection of the Appropriate DM Technique Training and Retraining with the Data Miner 86

4 x AGENT INTELLIGENCE THROUGH DATA MINING Part III Knowledge Diffusion: Three Representative Test Cases 6. DATA MINING ON THE APPLICATION LEVEL OF A MAS 93 1 Enterprise Resource Planning Systems 93 2 The Generalized Framework IRF Architecture Customer Order Agent type Recommendation Agent type Customer Profile Identification Agent type Supplier Pattern Identification Agent type Inventory Profile Identification Agent type Enterprise Resource Planning Agent type Installation and Runtime Workflows System Intelligence Benchmarking customer and suppliers IPIA products profile RA Intelligence An IRF Demonstrator Conclusions MINING AGENT BEHAVIORS Predicting Agent Behavior The Prediction Mechanism Applying «-Profile on MAS Modeling Agent Actions in an Operation Cycle Mapping Agent Actions to Vectors Evaluating Efficiency Profile efficiency evaluation Prediction system efficiency evaluation A Recommendation Engine Demonstrator System Parameters The fuzzy variable Time The fuzzy variable Frequency The output fuzzy variable Weight The Rules of the FIS Browsing through a Web Site Experimental Results Conclusions 133

5 Contents 3. MINING KNOWLEDGE FOR AGENT COMMUNITIES 1 Ecosystem Simulation 2 An Overview of Biotope 2.1 The Biotope Environment 2.2 The Biotope Agents Agent sight Agent movement Agent reproduction Agent communication - Knowledge exchange 2.3 Knowledge Extraction and Improvement Classifiers Classifier Evaluation mechanism Genetic Algorithm 2.4 The Assessment Indicators Environmental indicators Agent performance indicators 3 The Implemented Prototype 3.1 Creating a New Simulation Scenario 4 Experimental Results 4.1 Exploiting the Potential of Agent Communication Specifying the optimal communication rate Agent efficiency and knowledge base size Agent communication and unreliability 4.2 GAs in Unreliable Environments 4.3 Simulating Various Environments 5 Conclusions xi Part IV Extensions AGENT RETRAINING AND DYNAMICAL IMPROVEMENT OF AGENT INTELLIGENCE Formal Model Different Retraining Approaches Retraining in the Case of Classification Techniques Initial Training Retraining an Agent Type Retraining an Agent Instance Retraining in the Case of Clustering Techniques 169

6 xii AGENT INTELLIGENCE THROUGH DATA MINING 3.1 Initial Training Retraining Retraining in the Case of Association Rule Extraction Techniques Initial Training Retraining Retraining in the Case of Genetic Algorithms Experimental Results Intelligent Environmental Monitoring System Speech Recognition Agents The Iris Recommendation Agent Conclusions AREAS OF APPLICATION & FUTURE DIRECTIONS Areas of Application Environmental Monitoring Information Systems Agent Bidding and Auctioning Enhanced Software Processing Advanced AT-DM Symbiosis Architectures Distributed Agent Training Architectures Semantically-Aware Grid Architectures Summary and Conclusions Open Issues and Future Directions 185 References 189 Index 199 About The Authors 201

7 List of Figures 1.1 Mining for intelligence Agent-based applications and inference mechanisms Alternative routes for reading this book Technology evolution towards Data Mining A schematic representation of the KDD process The confluence of different technologies into DM A sample decision tree Deciding on the root node The clustering concept Intra- and inter-cluster similarity K-Means schematic representation The concepts of DBSCAN The Apriori algorithm Chromosome crossover Chromosome mutation The genetic algorithm mechanism The Nwana agent classification Alternative agent coordination schemes The structure of the reasoning agent Diagram of the Agent Academy development framework Creating the behavior of an agent through the Behavior Design Tool The unified MAS methodology The MAS development mechanism The common MAS development steps 75

8 xi v A GENT INTELLIGENCE THR O UGH DA TA MINING 5.4 Application level: the training framework The basic functionality of an agent prediction system The knowledge evaluation mechanism The training/retraining mechanism Launching Data Miner a Defining the ontology b Specifying the input file containing the training dataset Preprocessing data Selecting the proper DM technique and algorithm Tuning the selected algorithm Specifying training parameters Specifying output options The outcome of the data mining process The functionality of Data Miner The layers of IRF The IRF architectural diagram Installing IRF on top of an existing ERP The Workflow of SPIA RA order splitting policy The GUI of Customer Order Agent The final IPRA Recommendation The ^-Profile mechanism The evolution of an operation cycle Fuzzy variable time values Fuzzy variable frequency values Output variable weight values The main console of the demonstrator The generated agent recommendations Applying the WAVP metric on the extracted clusters An overview of the Biotope environment Agent vision field and the corresponding vision vector Deciding on the next move, based on the classifier set The possible paths towards the destination cell Establishing communication between neighboring agents Transforming the vision vector into a bit stream Creating new Classifiers 145

9 List of Figures xv 8.8 Configuring environmental parameters Configuring agent parameters Biotope "in action" Comparing E^_3 and EA~ Comparing E^_ 5 and E^_ Comparing EA-7 and EAS Population growth with respect to varying GA application rate Convergence in the behaviors of agent communities when the GA application rate increases The food refresh rate plays a pivotal role in agent survival Retraining the agents of a MAS The O3RTAA system architecture The generalized EMIS architecture Improving the behavior of biding agents A software workflow process A common knowledge repository A semantically-aware architecture 183

10 List of Tables 2.1 Steps in the evolution of Data Mining The Play Golf dataset A sample transaction database The core features of Genetic Algorithms Environment characteristics with respect to agents The basic functionalities of each layer Techniques and algorithms provided by the Data Miner The IRF agent types and their functionality Fuzzification and Interestingness of dataset attributes Service Level and corresponding z Value IPRA inputs and outputs The resulting customer clusters The resulting supplier clusters The generated association rules IRF enhancements to ERPs Recommending the next action An example on predicting the next action A vector representing the operation cycle Mapping agent actions to vectors Fuzzification of input variable time Fuzzification of input variable frequency Fuzzification of output variable weight The resulting vector clusters The actions that comprise the profile of cluster Mapping the contents of Biotope 138

11 xviii AGENT INTELLIGENCE THROUGH DATA MINING 8.2 Perceiving the environment and taking action Agent actions and energy variation rate The application components of Biotope The application menu bar items Fixed parameter values for all the experiments Experiments on agent communication Average indicator values for experiments E^-i to E^_ Average indicator values for experiments E^_5 & E^_ Average indicator values for experiments EA-7 & E^_s Experiments on Genetic Algorithm application Average indicator values for experiments E^-i to Ej5_ Experiments on various environments Average indicator values for experiments Ec-i to Ec_io Retraining options for DIQ. D^Qi Retraining options for DpjQ i DQ Retraining options for DjQ i DQ Retraining options for DjQ i D^Qi DQ { Classification accuracies for the Diagnosis Agent Speech Recognition Agents Classification accuracy The Iris Recommendation Agent success 174