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Designing good pursuit problems as testbeds for distributed AI: A novel application of genetic algorithms

  • Mauro Manela
  • J. A. Campbell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 957)

Abstract

A basic N x M instance (game) of the Pursuit Problem is one in which N pursuing agents try to capture as many as possible of M prey agents by surrounding them, on a rectilinear grid. The 4 × 1 game has been considered as a testbed for comparing the effectiveness of different multiagent distributed architectures, and the 6 × 2 game has received a little attention. This paper reports a systematic exercise in evaluating the quality of pursuit games as potential testbeds for distributed artificial intelligence (DAI). Genetic algorithms (GAs) have been used both to optimise low-level architectural features of agents and to search the (N, M) space of games. The conclusion from experiments is that (M + 4) × M games have the right complexity to be good testbeds, provided that M > 4. Additionally, the paper demonstrates the usefulness of GAs as tools to help DAI designers, and argues that boredom is a concept that deserves consideration as a feature of general agent architectures.

Keywords

Autonomous Agent Success Ratio Agent Architecture Pursuit Problem Blue Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Mauro Manela
    • 1
  • J. A. Campbell
    • 1
  1. 1.Department of Computer ScienceUniversity College LondonLondonEngland

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