A Probabilistic Model of Action for Least-Commitment Planning with Information Gather

AI planning algorithms have addressed the problem of generating sequences of oper­ ators that achieve some input goal, usu­ ally assuming that the planning agent has perfect control over and information about the world. Relaxing these assumptions re­ quires an extension to the action represen­ tation that allows reasoning both about the changes an action makes and the information it provides. This paper presents an action representation that extends the determinis­ tic STRIPS model, allowing actions to have both causal and informational effects, both of which can be context dependent and noisy. We also demonstrate how a standard least­ commitment planning algorithm can be ex­ tended to include informational actions and contingent execution.