Probabilistic Plan Management
CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE
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The general problem of planning for uncertain domains remains a difficult challenge. Research that focuses on constructing plans by reasoning with explicit models of uncertainty has produced some promising mechanisms for coping with specific types of domain uncertainties however, these approaches generally have difficulty scaling. Research in robust planning has alternatively emphasized the use of deterministic planning techniques, with the goal of constructing a flexible plan or set of plans that can absorb deviations during execution. Such approaches are scalable, but either result in overly conservative plans, or ignore the potential leverage that can be provided by explicit uncertainty models. The main contribution of this work is a composite approach to planning that couples the strengths of both the above approaches while minimizing their weaknesses. Our approach, called Probabilistic Plan Management PPM, takes advantage of the known uncertainty model while avoiding the overhead of non-deterministic planning. PPM takes as its starting point a deterministic plan that is built with deterministic modeling assumptions. PPM begins by layering an uncertainty analysis on top of the plan. The analysis calculates the overall expected outcome of execution and can be used to identify expected weak areas of the schedule.
- Statistics and Probability