A Modular Structured Approach to Conditional Decision-Theoretic Planning

Liem Ngo, Peter Haddawy, and Hien Nguyen

A realistic system for planning with uncertain information in partially observable domains must be able to reason about sensing actions and to condition its further actions on the sensed information. Among implemented planning systems, we can distinguish two approaches to contingent decision-theoretic planning. The first is characterized by a highly unconstrained plan space, while the second is characterized by a constrained and inflexible specification of plan space. In this paper, we take a middle ground between these two approaches that we consider to be more practical. We permit the user to specify the structure of the space of possible plans to be considered but to do so in a flexible manner. This flexibility is obtained through the use of a modular representation. We separate the representation of actions from the representation of domain relations and we separate those from the representation of the plan space. Actions and domain relations are represented with schematic Bayes net fragments and plan space is represented using programming language constructs. We present a planning system that can find optimal plans given this representation.


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