As AI planning systems are applied to more realistic problems, the issue of choosing among alternative plans arises. The planner typically cannot prove that any alternative will or will not be successful, and so it much choose among plans on the basis of how likely they are to achieve their aims and how efficiently they do so. Decision theory supplies a normative model for making these choices, but is not directly applicable to the planning problem as AI has posed it, and more importantly does not provide a computational framework within which to make the choices. In particular, decision theory offers no advice on how to generate or modify plans. Decision theory and symbolic AI planning algorithms therefore offer complementary strengths: the former provides a rich representation for expressing choice among alternatives and a theory of how to make those choices rationally; the latter provides a computational method for generating and improving plans, but under restrictive assumptions. The two approaches will be integrated to produce a practical decision-theoretic planner. The research contains theoretical and computational; aspects, the former oriented toward reconciling the decision-theoretic and AI problem representations, the latter oriented toward developing an algorithm that efficiently builds, evaluated, and chooses among plan alternatives.
