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Autonomous learning of sequential tasks: experiments and analyses.

R Sun1, T Peterson

  • 1NEC Research Institute, Princeton, NJ 08540, USA.

IEEE Transactions on Neural Networks
|February 8, 2008
PubMed
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This study introduces the CLARION model, a hybrid approach integrating neural, reinforcement, and symbolic learning for sequential decision tasks. It demonstrates advantages in on-line, bottom-up learning from neural to symbolic representations.

Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Machine Learning

Background:

  • Hybrid learning models offer potential for integrating diverse knowledge representations.
  • Bridging neural and symbolic learning is crucial for comprehensive AI systems.
  • Sequential decision tasks require robust learning mechanisms for effective performance.

Purpose of the Study:

  • To present the CLARION model, a novel hybrid learning system.
  • To integrate neural, reinforcement, and symbolic learning paradigms.
  • To investigate the model's efficacy in sequential decision-making.

Main Methods:

  • Developed a two-level hybrid model named CLARION.
  • Integrated neural, reinforcement, and symbolic learning components.

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  • Employed on-line, bottom-up learning from neural to symbolic representations.
  • Utilized both procedural (neural) and declarative (symbolic) knowledge.

    • Main Results:

    • The CLARION model successfully handled sequential decision tasks.
    • Experimental analyses highlighted the model's advantages.
    • Demonstrated effective integration of procedural and declarative knowledge.
    • Showcased the benefits of bottom-up learning from neural to symbolic levels.

    Conclusions:

    • The CLARION model provides a synergistic approach to learning.
    • Hybrid models can effectively combine different learning strategies.
    • The model shows promise for complex sequential decision problems.