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On Integrating Machine Learning with Planning

  • Gerald F. DeJong
  • Melinda T. Gervasio
  • Scott W. Bennett
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 195)

Abstract

Domain-independent classical planning is faced with serious difficulties. Many of these are traceable to some facet of the frame problem. Perfect knowledge of the planner’s world and operators is impossible for most domains. With anything less, small unmodeled errors can result in large discrepancies between the expected and observed worlds. Such discrepancies may interfere with the achievement of a goal. Reactivity provides an extreme response. It allows only sensor information after an action is taken to judge the action’s effects, and abandons projection altogether. There must be a vast space of possibilities between the extremes of classical planning and reactivity. This paper describes two called computable planning and permissive planning. Machine learning, in the form of Explanation-Based Learning, is used in computable planning to recognize deferrable goals, resulting in many of the benefits offered by reactivity but in a domain independent form. In permissive planning, machine learning techniques are used to refine plans through experience so that they become less sensitive to the necessarily approximate knowledge.

Keywords

Plan Component Failure Recovery Planning Concept Repeat Loop Classical Planning 
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

  • Gerald F. DeJong
    • 1
  • Melinda T. Gervasio
    • 1
  • Scott W. Bennett
    • 1
  1. 1.Beckman Institute & Computer Science DepartmentUniversity of IllinoisUrbana

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