The Role of Self-Models in Learning to Plan

  • Gregg Collins
  • Lawrence Birnbaum
  • Bruce Krulwich
  • Michael Freed
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 195)


We argue that in order to learn to plan effectively, an agent needs an explicit model of its own planning and plan execution processes. Given such a model, the agent can pinpoint the elements of these processes that are responsible for an observed failure to perform as expected, which in turn enables the formulation of a repair designed to ensure that similar failures do not occur in the future. We have constructed simple models of a number of important components of an intentional agent, including threat detection, execution scheduling, and projection, and applied them to learning within the context of competitive games such as chess and checkers.


Fault Diagnosis Priority Queue Plan Execution Intentional Agent General Problem Solver 
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

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • Gregg Collins
    • 1
  • Lawrence Birnbaum
    • 1
  • Bruce Krulwich
    • 1
  • Michael Freed
    • 1
  1. 1.The Institute for the Learning SciencesNorthwestern UniversityEvanston

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