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Agent architecture based on an interactive self-reflection classifier system

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Abstract

This paper extends the learning classifier system (LCS) to introduce a mechanism for recognizing a current situation by determining a boundary between self and others, and investigates its capability through interaction with an agent. Intensive simulations for adapting to an interacting agent have revealed the following implications: (1) the proposed architecture adapts to an interacting agent more quickly and appropriately than the conventional LCS, and (2) our architecture keeps its adaptation to an interacting agent even when the agent changes its internal models before our architecture acquires it completely.

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References

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Author information

Authors and Affiliations

  1. Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, 226-8502, Yokohama, Japan

    Keiki Takadama

  2. Kyoto University, Kyoto, Japan

    Hiroyasu Inoue & Osamu Katai

  3. ATR Human Information Science Laboratories, Kyoto, Japan

    Keiki Takadama & Katsunori Shimohara

  4. ATR Intelligent Robotics and Communication Laboratories, Kyoto, Japan

    Michio Okada

Authors
  1. Keiki Takadama
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  2. Hiroyasu Inoue
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  3. Katsunori Shimohara
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  4. Michio Okada
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  5. Osamu Katai
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Corresponding author

Correspondence to Keiki Takadama.

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Cite this article

Takadama, K., Inoue, H., Shimohara, K. et al. Agent architecture based on an interactive self-reflection classifier system. Artif Life Robotics 5, 103–108 (2001). https://doi.org/10.1007/BF02481347

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  • DOI: https://doi.org/10.1007/BF02481347

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