Agent Intelligence Through Data Mining Multiagent Systems Artificial Societies and Simulated Organizations 14 1st edition by Andreas Symeonidis, Pericles Mitkas ISBN 0387243526  978-0387243528

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ISBN 10: 0387243526
ISBN 13: 978-0387243528
Author: Andreas Symeonidis, Pericles Mitkas 

Knowledge, hidden in voluminous data repositories routinely created and maintained by today’s applications, can be extracted by data mining. The next step is to transform this discovered knowledge into the inference mechanisms or simply the behavior of agents and multi-agent systems. Agent Intelligence Through Data Mining addresses this issue, as well as the arguable challenge of generating intelligence from data while transferring it to a separate, possibly autonomous, software entity. This book contains a methodology, tools and techniques, and several examples of agent-based applications developed with this approach. This volume focuses mainly on the use of data mining for smarter, more efficient agents.

Agent Intelligence Through Data Mining is designed for a professional audience of researchers and practitioners in industry. This book is also suitable for graduate-level students in computer science.

Agent Intelligence Through Data Mining (Multiagent Systems, Artificial Societies, and Simulated Organizations 14) 1st Table of contents:

Preface

• Overview of Agent Intelligence and Data Mining
• Relevance of Multi-Agent Systems in Modern AI
• Goals of the Book: Bridging Data Mining with Agent-Based Intelligence
• Intended Audience and Prerequisites

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Part I: Introduction to Multi-Agent Systems and Data Mining

1. Introduction to Multi-Agent Systems (MAS)

   • Definition and Key Concepts of Multi-Agent Systems
   • Types of Agents: Reactive, Deliberative, Hybrid
   • Communication, Coordination, and Cooperation Among Agents
   • Applications of MAS in Real-World Problems (E-commerce, Robotics, Healthcare)
   • Challenges in MAS: Distributed Problem Solving, Autonomy, and Coordination

2. Fundamentals of Data Mining

   • Overview of Data Mining: Concepts and Techniques
   • Data Preprocessing: Cleaning, Transformation, and Normalization
   • Key Data Mining Tasks: Classification, Clustering, Regression, Association Rules
   • Algorithms for Data Mining: Decision Trees, Neural Networks, K-means, etc.
   • Evaluation Metrics: Precision, Recall, F1-Score, ROC Curves

3. Linking Data Mining with Agent Intelligence

   • Role of Data in Enhancing Agent Intelligence
   • How Agents Can Use Data Mining for Learning and Adaptation
   • Data Mining for Decision Support in MAS
   • Integrating Data Mining Algorithms with Multi-Agent Frameworks
   • Learning from Data: Supervised vs. Unsupervised Approaches in Agent Systems

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Part II: Agent Models and Data Mining Techniques

4. Learning Agents: Types and Architectures

   • Reactive Agents: Simple Reflex and Model-Based Reflex Agents
   • Deliberative Agents: Planning, Reasoning, and Decision-Making
   • Hybrid Agents: Combining Deliberative and Reactive Behavior
   • Machine Learning in Agents: Reinforcement Learning, Q-learning, and Policy Search
   • Data Mining-Enabled Agent Architectures

5. Data Mining for Agent Decision Making

   • Decision Trees and Rule Induction in Agent Systems
   • Clustering for Autonomous Grouping and Categorization
   • Classification: Teaching Agents to Make Predictions
   • Neural Networks in Agents: Self-Organizing Maps and Deep Learning
   • Case-Based Reasoning: Data Mining with Historical Data for Future Decisions

6. Agent-Based Learning Using Data Mining Techniques

   • Reinforcement Learning: Data Mining for Reward-Based Learning
   • Unsupervised Learning and Clustering for Agent Exploration
   • Association Rule Mining for Knowledge Discovery in MAS
   • Data-Driven Approach to Adaptation and Evolution in Agent Systems
   • Combining Learning Algorithms with Data Mining for Effective Agent Behavior

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Part III: Advanced Agent Intelligence with Data Mining

7. Data Mining for Agent Coordination and Collaboration

   • Collaborative Filtering: Agents Sharing Information and Learning from Each Other
   • Data Mining in Negotiation and Resource Allocation
   • Agents’ Collective Behavior: Aggregating Knowledge and Making Group Decisions
   • Distributed Data Mining in MAS: Federated Learning Approaches
   • Cooperative vs. Competitive Learning in Multi-Agent Environments

8. Multi-Agent Data Mining Systems

   • Concept of Multi-Agent Data Mining (MADM)
   • Distributed Data Mining: Applying MAS for Large-Scale Data Analysis
   • Frameworks for Multi-Agent Data Mining Systems
   • Self-Organizing Agents for Data Discovery and Mining
   • The Role of Coordination Protocols in Multi-Agent Data Mining Systems

9. Scalable and Robust Agent-Based Data Mining Systems

   • Scalability Challenges in Multi-Agent Data Mining
   • Parallel and Distributed Data Mining in MAS
   • Robustness of Agent Systems: Handling Noise and Uncertainty
   • Error Detection and Data Quality in Multi-Agent Learning
   • Efficient Knowledge Discovery in Large-Scale Agent Systems

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Part IV: Applications of Agent Intelligence Through Data Mining

10. Applications in E-commerce and Consumer Behavior

    • Personalized Recommendations through Multi-Agent Systems
    • Data Mining for Market Analysis and Trend Prediction
    • Collaborative Filtering and Social Network Mining in E-commerce
    • Intelligent Pricing and Auctions in Agent-Based Marketplaces
    • Case Study: Smart Shopping Assistants and Intelligent Agents

11. Healthcare and Medical Applications

    • Data Mining for Medical Diagnosis and Decision Support
    • Multi-Agent Systems in Healthcare: Personalized Treatment and Care
    • Agent-Based Modeling of Disease Spread and Public Health
    • Patient Data Mining for Predictive Health Monitoring
    • Case Study: AI-Based Healthcare Agents for Diagnostics and Treatment

12. Robotics and Autonomous Systems

    • Multi-Agent Robotics: Coordinating Autonomous Robots for Complex Tasks
    • Data Mining for Path Planning and Obstacle Avoidance in Robotics
    • Collaborative Learning Among Robots Using Distributed Data
    • Machine Learning Algorithms for Real-Time Robot Adaptation
    • Case Study: Autonomous Vehicles and Swarm Robotics

13. Smart Cities and Internet of Things (IoT)

    • Data Mining for Smart Infrastructure and Urban Planning
    • Multi-Agent Systems for Resource Management in Smart Cities
    • Agents and Data Mining for Traffic Optimization and Smart Grids
    • Collaborative Agents in IoT Networks: Learning and Data Sharing
    • Case Study: IoT and Multi-Agent Systems in Smart City Applications

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Part V: Challenges and Future Directions

14. Ethical and Security Issues in Agent-Based Data Mining

    • Privacy and Security Concerns in Data Mining with Multi-Agent Systems
    • Ethical Dilemmas in Autonomous Agents Making Decisions Based on Data
    • Bias in Agent Learning: Addressing Data-Driven Discrimination
    • Accountability and Transparency in Agent-Driven Decision Making
    • Legal and Social Implications of Data Mining in MAS

15. Future Trends in Agent Intelligence and Data Mining

    • The Role of Deep Learning and AI in Agent Intelligence
    • Hybrid Systems: Combining Agent-Based Modeling with Advanced Data Mining
    • Evolution of Autonomous Agents with Adaptive Learning Capabilities
    • The Convergence of Multi-Agent Systems, Big Data, and IoT
    • Autonomous Organizations and Artificial Societies: Future Possibilities

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Conclusion

• Recap of the Synergy Between Multi-Agent Systems and Data Mining
• The Potential for Advancements in Agent Intelligence
• Final Thoughts on the Future of Agent-Based Data Mining Systems

References

• Key Publications, Research Articles, and Resources for Further Reading

Index

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