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Planning with external events

Jim Blythe, in proceedings of the conference on Uncertainty in AI, 1994
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Abstract

I describe a planning methodology for domains with uncertainty in the
form of external events that are not completely predictable. The
events are represented by enabling conditions and probabilities of
occurrence. The planner is goal-directed and backward chaining, but
the subgoals are suggested by analysing the probability of success of
the partial plan rather than being simply the open conditions of the
operators in the plan. The partial plan is represented as a Bayesian
belief net to compute its probability of success. Since calculating
the probability of success of a plan can be very expensive I introduce
two other techniques for computing it, one that uses Monte Carlo
simulation to estimate it and one based on a Markov chain
representation that uses knowledge about the dependencies between the
predicates describing the domain.

postscript