Probabilistic planning with information gathering and contingent execution

Abstract

Most AI representations and algorithms for plan gen-eration have not included the concept of information-producing actions (also called diagnostics, or tests, in the decision making literature). We present a planning representation and algorithm that models information-producing actions and constructs plans that exploit the information produced by those ac-tions. We extend the buridan (Kushmerick et al. 1994) probabilistic planning algorithm, adapting the action representation to model the behavior of imper-fect sensors, and combine it with a framework for con-tingent action that extends the cnlp algorithm (Peot and Smith 1992) for conditional execution. The result, c-buridan, is an implemented planner that builds plans with probabilistic information-producing actions and contingent execution.