You are previewing Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies.
O'Reilly logo
Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies

Book Description

Biologically inspired computation methods are growing in popularity in intelligent systems, creating a need for more research and information. Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization compiles numerous ongoing projects and research efforts in the design of agents in light of recent development in neurocognitive science and quantum physics. This innovative collection provides readers with interdisciplinary applications of multi-agents systems, ranging from economics to engineering.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Preface
    1. ABSTRACT
    2. 1. GENERAL BACKGROUND
    3. 2. MULTI-AGENT FINANCIAL SYSTEMS
    4. 3. NEURO-INSPIRED AGENTS
    5. 4 BIO-INSPIRED AGENT-BASED ARTIFICIAL MARKETS
    6. 5 MUTLI-AGENT ROBOTICS
    7. 6 MULTI-AGENT GAMES AND SIMULATION
    8. 7 MULTI-AGENT LEARNING
    9. 8 MISCELLANEOUS
    10. 7. CONCLUDING REMARKS
  5. Acknowledgment
  6. Section 1: Multi-Agent Financial Decision Systems
    1. Chapter 1: A Multi-Agent System Forecast of the S&P/Case-Shiller LA Home Price Index
      1. ABSTRACT
      2. INTRODUCTION
      3. THE S&P/CASE-SHILLER INDEX
      4. METHODOLOGY
      5. ESTIMATION RESULTS
      6. FORECAST RESULTS
      7. CONCLUSION
    2. Chapter 2: An Agent-based Model for Portfolio Optimization Using Search Space Splitting
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORKS
      4. PORTFOLIO OPTIMIZATION PROBLEM
      5. AGENT-BASED MODEL
      6. SIMULATION RESULTS
      7. CONCLUSION
  7. Section 2: Neuro-Inspired Agents
    1. Chapter 3: Neuroeconomics
      1. ABSTRACT
      2. NEUROECONOMICS: AN ACE VIEWPOINT
      3. PREFERENCE
      4. VALUE AND CHOICE
      5. LEARNING AND THE DRPE HYPOTHESIS
      6. DUAL SYSTEM CONJECTURE
      7. FROM MODULAR MIND/BRAIN TO MODULAR PREFERENCE
      8. CONCLUDING REMARKS: AGENT BASED OR BRAIN BASED?
    2. Chapter 4: Agents in Quantum and Neural Uncertainty
      1. ABSTRACT
      2. INTRODUCTION
      3. CONCEPTS AND DEFINITIONS
      4. FRAMEWORK OF FIRST ORDER OF CONFLICT LOGIC STATE
      5. QUANTUM MECHANICS: SUPERPOSITION AND FIRST ORDER CONFLICTS
      6. NEURAL IMAGE OF AUT
      7. CONCLUSION
  8. Section 3: Bio-Inspired Agent-Based Artificial Markets
    1. Chapter 5: Bounded Rationality and Market Micro-Behaviors
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. RELATED WORK
      5. THE DA MARKET ENVIRONMENT
      6. THE AGENT-BASED GP SYSTEM
      7. RESULTS AND ANALYSIS
      8. CONCLUDING REMARKS
    2. Chapter 6: Social Simulation with Both Human Agents and Software Agents
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE REVIEW
      4. EXPERIMENTAL DESIGN
      5. ANALYSIS OF SIMULATIONS AND HUMAN EXPERIMENTS
      6. CONCLUDING REMARKS
    3. Chapter 7: Evolution of Agents in a Simple Artificial Market
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED LITERATURE
      4. INFERRING UTILITY FUNCTIONS OF SUCCESSES AND FAILURES
      5. AN AGENT-BASED FINANCIAL MARKET MODEL
      6. HYPOTHETICAL VALIDATION USING AN AGENT-BASED MODEL
      7. SIMULATION RESULTS
      8. IMPLICATION OF SIMULATION RESULTS
      9. SUMMARY AND FUTURE WORK
    4. Chapter 8: Agent-Based Modeling Bridges Theory of Behavioral Finance and Financial Markets
      1. ABSTRACT
      2. INTRODUCTION
      3. DESCRIPTION OF AN AGENT-BASED FINANCIAL MARKET MODEL
      4. HOW PASSIVE AND ACTIVE INVESTORS BEHAVE
      5. TRADING WITH FUNDAMENTALISTS, PASSIVE INVESTORS, AND OVER CONFIDENT INVESTORS, AND INVESTORS WITH PROSPECT THEORY
      6. SUMMARY AND CONCLUSION
      7. Appendices
  9. Section 4: Multi-Agent Robotics
    1. Chapter 9: Autonomous Specialization in a Multi-Robot System using Evolving Neural Networks
      1. ABSTRACT
      2. INTRODUCTION
      3. COOPERATIVE PACKAGE PUSHING PROBLEM
      4. COMPUTER SIMULATIONS
      5. MBEANN
      6. CONCLUSION
    2. Chapter 10: A Multi-Robot System Using Mobile Agents with Ant Colony Clustering
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE MOBILE AGENTS
      5. ANT COLONY CLUSTERING
      6. THE ROBOTS
      7. NUMERICAL EXPERIMENTS
      8. CONCLUSION AND FUTURE DIRECTIONS
  10. Section 5: Multi-Agent Games and Simulations
    1. Chapter 11: The AGILE Design of Reality Game AI
      1. ABSTRACT
      2. INTRODUCTION
      3. THE LAKE STANLEY LAND BRIDGE
      4. THE AGILE METHODOLOGY
      5. GAME DEVELOPMENT
      6. RESULTS
      7. FUTURE WORK
    2. Chapter 12: Management of Distributed Energy Resources Using Intelligent Multi-Agent System
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. IMPLEMENTATION OF MULTI AGENT SYSTEM
      5. SIMULATION OF DEVELOPED SOFTWARE
      6. RELIABILITY CHECKING OF MICROGRID
      7. RESULTS AND DISCUSSION
      8. CONCLUSION
  11. Section 6: Multi-Agent Learning
    1. Chapter 13: Effects of Shaping a Reward on Multiagent Reinforcement Learning
      1. ABSTRACT
      2. INTRODUCTION
      3. PROBLEM DOMAIN
      4. COMPONENTS OF REINFORCEMENT LEARNING
      5. EXPERIMENT
      6. DISCUSSION
      7. CONCLUSION
    2. Chapter 14: Swarm Intelligence Based Reputation Model for Open Multi agent Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. SWARM INTELLIGENCE
      4. THE ALGORITHM
      5. RESULTS
      6. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
    3. Chapter 15: Exploitation-oriented Learning XoL
      1. ABSTRACT
      2. INTRODUCTION
      3. PROBLEM FORMULATIONS
      4. RATIONALITY OF PROFIT SHARING
      5. PROFIT SHARING IN MULTI-AGENT ENVIRONMENTS
      6. EXPANSION OF PS TO POMDPS
      7. CONCLUSION
      8. Appendix A
      9. Appendix B
      10. Appendix C
  12. Section 7: Miscellaneous
    1. Chapter 16: Pheromone-style Communication for Swarm Intelligence
      1. ABSTRACT
      2. INTRODUCTION
      3. ANT WAR AS COMPETITIVE ENVIRONMENT
      4. DECISION-MAKING
      5. EVOLUTIONARY PROCESS
      6. EXPERIMENT
      7. RESULTS
      8. CONCLUSION
    2. Chapter 17: Evolutionary Search for Cellular Automata with Self-Organizing Properties toward Controlling Decentralized Pervasive Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. TASK
      4. EVOLUTIONARY SEARCH BY GENETIC ALGORITHM
      5. CONCLUSION
  13. Compilation of References
  14. About the Contributors