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Planning and decision theory

Published online by Cambridge University Press:  07 July 2009

Peter Haddawy  and
Larry Rendell
Peter Haddawy
Affiliation:
University of Illinois, Dept of Computer Science, 1304 W. Springfield, Urbana, IL 61801, USA
Larry Rendell
Affiliation:
University of Illinois, Dept of Computer Science, 1304 W. Springfield, Urbana, IL 61801, USA
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Research on planning in AI can be separated into the two major areas: plan generation and plan representation. Most AI planners to date have been based on the STRIPS planning representation. This representation has a number of limitations. Much recent work in plan representation has addressed these limitations. It was shown that Decision Theory can be used to remove a number of the limitations. Furthermore, the decision theoretic framework provides a precise definition of rational behaviour. There remain open questions within decision theory regarding belief revision and causality. It should be noted that these problems are not artifacts of the representation. Rather they arise because the rich representation allows their formulation. Some work integrating AI and decision theoretic approaches to planning has been done but this remains a largely untouched research area.

We see two main avenues for fruitful research. First, the straightforward decision theoretic formulation of planning is computationally impractical. Techniques need to be developed to do efficient decision theoretic planning. Work in AI plan generation has exploited information contained the structure of qualitative representations to guide efficient plan construction. These techniques should be applied to decision theoretic representations as well. Second, AI has developed many representations that allow useful structuring of knowledge about the world. Decision Theory has concentrated on representing beliefs and desires. Integration of AI and decision theoretic representations would yield powerful representation languages. Some of the benefits of such work can already be seen in the research combining temporal and decision theoretic representations.

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Type
Research Article
Information
The Knowledge Engineering Review , Volume 5 , Issue 1 , March 1990 , pp. 15 - 33
Copyright
Copyright © Cambridge University Press 1990

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