A Subsymbolic and Symbolic Model for Learning Sequential Decision Tasks

  • Ron Sun
  • Todd Peterson
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 59)


For dealing with reactive sequential decision tasks, a learning model Clarion was developed, which is a hybrid connectionist model consisting of both localist (symbolic) and distributed representations, based on the two-level approach proposed in Sun (1995). The model learns and utilizes procedural and declarative knowledge, tapping into the synergy of the two types of processes. It unifies neural, reinforcement, and symbolic methods to perform on-line, bottom-up learning (from subsymbolic to symbolic knowledge). Experiments in various situations shed light on the working of the model. Its theoretical implications in terms of symbol grounding are also discussed.


Symbolic Representation Bottom Level Inductive Logic Programming Declarative Knowledge Navigation Task 
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

© Physica-Verlag Heidelberg 2001

Authors and Affiliations

  • Ron Sun
    • 1
    • 2
  • Todd Peterson
    • 1
    • 2
  1. 1.NEC Research InstitutePrincetonUSA
  2. 2.The University of AlabamaTuscaloosaUSA

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