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Embedding Connectionist Autonomous Agents in Time: The ‘Road Sign Problem’

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Abstract

This paper identifies a problem of significance for approaches to adaptive autonomous agent research seeking to go beyond reactive behaviour without resorting to hybrid solutions. The feasibility of recurrent neural network solutions are discussed and compared in the light of experiments designed to test ability to handle long-term temporal dependencies, in a more situated context than hitherto. It is concluded that a general-purpose recurrent network with some processing enhancements can begin to fulfil the requirements of this non-trivial problem.

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Authors and Affiliations

  1. Department of Computer Science, De Montfort University, Leicester, U.K.

    R. M. Rylatt

  2. Department of Computer Science, De Montfort University, Leicester, U.K.

    C. A. Czarnecki

Authors
  1. R. M. Rylatt
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  2. C. A. Czarnecki
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Rylatt, R.M., Czarnecki, C.A. Embedding Connectionist Autonomous Agents in Time: The ‘Road Sign Problem’. Neural Processing Letters 12, 145–158 (2000). https://doi.org/10.1023/A:1009645229062

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