A Class of Algorithms for Distributed Constraint Optimization 1st Edition by Adrian Petcu ISBN 158603989X 9781586039899

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ISBN 10: 158603989X
ISBN 13: 9781586039899
Author: Adrian Petcu

Multi Agent Systems (MAS) have recently attracted a lot of interest because of their ability to model many real life scenarios where information and control are distributed among a set of different agents. Practical applications include planning, scheduling, distributed control, resource allocation etc. A major challenge in such systems is coordinating agent decisions, such that a globally optimal outcome is achieved. Distributed Constraint Optimization Problems (DCOP) are a framework that recently emerged as one of the most successful approaches to coordination in MAS. A Class of Algorithms for Distributed Constraint Optimization addresses three major issues that arise in DCOP: efficient optimization algorithms, dynamic and open environments and manipulations from self-interested users. It makes significant contributions in all these directions by introducing a series of DCOP algorithms, which are based on dynamic programming and largely outperform previous DCOP algorithms. The basis of this class of algorithms is DPOP, a distributed algorithm that requires only a linear number of messages, thus incurring low networking overhead. For dynamic environments, self-stabilizing algorithms that can deal with changes and continuously update their solutions, are introduced. For self interested users, the author proposes the M-DPOP algorithm, which is the first DCOP algorithm that makes honest behavior an ex-post Nash equilibrium by implementing the VCG mechanism distributedly. The book also discusses the issue of budget balance and mentions two algorithms that allow for redistributing (some of) the VCG payments back to the agents, thus avoiding the welfare loss caused by wasting the VCG taxes.

A Class of Algorithms for Distributed Constraint Optimization 1st Edition Table of contents:

Part I: Theoretical Foundations of Distributed Constraint Optimization

2. Fundamentals of Distributed Constraint Optimization

   • Definition of DCOPs and Key Concepts
   • Constraints and Variables in Distributed Systems
   • The Role of Agents in Distributed Constraint Solving
   • Decentralized vs. Centralized Optimization
   • Key Properties and Challenges in DCOPs

3. Communication and Coordination in DCOPs

   • Communication Models for Distributed Systems
   • Coordination Protocols in Multi-Agent Systems
   • Distributed Consensus and Synchronization
   • Trade-offs Between Communication Overhead and Solution Quality
   • Case Studies in Coordination and Cooperation

4. Search Algorithms for DCOPs

   • Search Algorithms in Centralized Optimization
   • Modifying Search Algorithms for Distributed Settings
   • Depth-First, Breadth-First, and Best-First Search in DCOPs
   • Local Search Algorithms for Distributed Systems
   • Heuristics and Pruning Techniques in Distributed Search

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Part II: Algorithms for Distributed Constraint Optimization

5. The ADOPT Algorithm

   • Introduction to the ADOPT Algorithm
   • Agent Communication in ADOPT
   • Local Search and Distributed Backtracking
   • Optimization and Convergence Criteria in ADOPT
   • Performance Evaluation and Applications of ADOPT

6. The DPOP Algorithm

   • Overview of Distributed Pseudotree Optimization (DPOP)
   • Structure of DPOP and Message Passing Protocols
   • Distributed Constraint Propagation in DPOP
   • DPOP’s Efficiency and Scalability
   • Applications of DPOP in Large-Scale Systems

7. The SODA Algorithm

   • Introduction to the SODA (Solution-Oriented Distributed Algorithm)
   • Algorithm Design and Implementation Details
   • Communication Complexity and Solution Quality in SODA
   • Comparative Analysis with Other DCOP Algorithms
   • Practical Applications of SODA in Multi-Agent Coordination

8. Hybrid Approaches to DCOPs

   • Combining Centralized and Distributed Techniques
   • Hybrid Algorithms for DCOPs: Trade-offs and Benefits
   • Local vs. Global Search Strategies
   • Multi-Objective Optimization in DCOPs
   • Adaptive Methods for Dynamic Distributed Environments

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Part III: Advanced Topics and Applications

9. Dynamic and Multi-Agent DCOPs

   • Handling Dynamic Changes in Distributed Environments
   • Multi-Agent Cooperation and Competition in DCOPs
   • Dynamic Resource Allocation and Scheduling
   • Adaptive Algorithms for Real-Time DCOPs
   • Applications in Autonomous Systems and Robotics

10. DCOPs in Real-World Applications

    • Applications in Sensor Networks and Internet of Things (IoT)
    • Distributed Resource Allocation in Smart Grids
    • Multi-Agent Systems for Traffic Control and Logistics
    • Distributed Planning and Scheduling in Industry
    • Case Studies of DCOP Algorithms in Practice

11. Scalability and Efficiency in DCOP Algorithms

    • Scalability Issues in Large-Scale Distributed Systems
    • Techniques for Improving Algorithm Efficiency
    • Memory and Computational Complexity in DCOP Algorithms
    • Optimizing Communication Overheads
    • Evaluation Metrics for Scalability and Performance

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Part IV: Future Directions and Challenges

12. Future Research Directions in DCOP Algorithms

    • New Theoretical Approaches to DCOPs
    • Combining DCOPs with Machine Learning Techniques
    • Decentralized Deep Learning in Multi-Agent Systems
    • The Role of DCOPs in Autonomous Systems and AI
    • Emerging Applications of DCOP Algorithms

13. Challenges and Open Problems in Distributed Constraint Optimization

    • Open Problems in Distributed Optimization
    • Robustness and Fault-Tolerance in DCOPs
    • Coordination in Uncertainty and Incomplete Information
    • The Future of Multi-Agent Systems and Distributed AI
    • Closing Thoughts and Summary

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