A Probabilistic Model of Action for Least-Commitment Planning with
  Information Gather
  AI planning algorithms have addressed the problem of generating sequences of
operators that achieve some input goal, usually assuming that the planning
agent has perfect control over and information about the world. Relaxing these
assumptions requires an extension to the action representation that allows
reasoning both about the changes an action makes and the information it
provides. This paper presents an action representation that extends the
deterministic 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
extended to include informational actions and contingent execution.
