Abstract

AI planning agents are goal-directed: success is measured in terms of whether or not an input goal is satisfied, and the agent's computational processes are driven by those goals. A decision-theoretic agent, on the other hand, has no explicit goals--- success is measured in terms of its preferences or a utility function that respects those preferences. The two approaches have complementary strengths and weaknesses. Symbolic planning provides a computational theory of plan generation, but under unrealistic assumptions: perfect information about and control over the world and a restrictive model of actions and goals. Decision theory provides a normative model of choice under uncertainty, but offers no guidance as to how the planning options are to be generated. This paper unifies the two approaches to planning by describing utility models that support rational decision making while retaining the goal information needed to support plan generation. We develop an extended model of goals tha...

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