Probabilistic Planning with Information Gathering and Contingent Execution

Denise Draper, Steve Hanks, and Daniel Weld

Most AI representations and algorithms for plan generation have not included the concept of information-producing actions (also called diagnostics, or tests, in the decision making literature). We present planning representation and algorithm that models information-producing actions and constructs plans that exploit the information produced by those actions. We extend the BURIDAN (Knshmerick et al. 1994) probabilistic planning algorithm, adapting the action representation to model the behavior of imperfect sensors, and combine it with a framework for contingent action that extends the CNLP algorithm (Peot and Smith 1992) for conditioned execution. The result, C-BURIDAN, is an implemented planner that builds plans with probabilistic information-producing actions and contingent execution.


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