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Agent Compromises in Distributed Problem Solving

  • Yi Tang
  • Jiming Liu
  • Xiaolong Jin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2690)

Abstract

ERA is a multi-agent oriented method for solving constraint satisfaction problems [5]. In this method, agents make decisions based on the information obtained from their environments in the process of solving a problem. Each agent has three basic behaviors: least-move, better-move, and random-move. The random-move is the unique behavior that may help the multi-agent system escape from a local minimum. Although random-move is effective, it is not efficient. In this paper, we introduce the notion of agent compromise into ERA and evaluate its effectiveness and efficiency through solving some benchmark Graph Coloring Problems (GCPs). When solving a GCP by ERA, the edges are transformed into two types of constraints: local constraints and neighbor constraints. When the system gets stuck in a local minimum, a compromise of two neighboring agents that have common violated neighbor constraints may be made. The compromise can eliminate the original violated neighbor constraints and make the two agents act as a single agent. Our experimental results show that agent compromise is an effective and efficient technique for guiding a multi-agent system to escape from a local minimum.

Keywords

Agent Compromises Distributed Constraint Satisfaction Problem Graph Coloring Problem Distributed GCP Solving 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Yi Tang
    • 1
    • 2
  • Jiming Liu
    • 3
  • Xiaolong Jin
    • 3
  1. 1.Department of MathematicsZhongshan UniversityGuangzhouChina
  2. 2.Department of MathematicsGuangzhou UniversityGuangzhouChina
  3. 3.Department of Computer ScienceHong Kong Baptist UniversityHong Kong

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