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Comparing the Learning Processes of Cognitive Distance Learning and Search Based Agent

  • Hiroshi Yamakawa
  • Yuji Miyamoto
  • Hiroyuki Okada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2084)

Abstract

Our proposed cognitive distance learning agent generates sequence of actions from a start state to goal state in problem state space. This agent learns cognitive distance (path cost) of arbitrary combination of two states. The action generation at each state is selection of next state that has minimum cognitive distance to the goal.

In this paper, we investigate a leraning process of the agent by a computer simulation inatile world state space. An average search cost is more reduced more the prior learning term is long and our problem solve is familiar to the environment. After enough learning process, an average search cost of prposed method is reduced to 1/20 from that of conventional search method.

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References

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    Richard S. Sutton and Andrew G. Barto: Reinforcement Learning: An Introduction, Adaptive Computation and Machine Learning. MIT Press (1988).Google Scholar
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    L. P. Kaelbling and et al: Reinforcement Learning: A Survey. J. Artificial Intelligence Research. 4 237–285 (1996).Google Scholar
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    H. Yamakawa and et al: Proposing Problem Solver using Cognitive Distance, Proc. MACC2000, (2000).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Hiroshi Yamakawa
    • 1
  • Yuji Miyamoto
    • 2
  • Hiroyuki Okada
    • 1
  1. 1.Real World Computing PartnershipChibaJapan
  2. 2.FUJITSU BROAD SOLUTION & CONSULTING Inc.TokyoJapan

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