Combining Knowledge Compilation and Search for Conformant Probabilistic Planning

We present a new algorithm for conformant probabilistic planning, which for a given horizon produces a plan that maximizes the probability of success under quantified uncertainty about the initial state and action effects, and absence of sensory information. Recent work has studied systematic search in the space of all candidate plans as a feasible approach to conformant probabilistic planning, but the algorithms proposed require caching of intermediate computations in such a way that memory is often exhausted quickly except for small planning horizons. On the other hand, planning problems in typical formulations generally have treewidths that do not grow with the horizon, as connections between variables are local to the neighborhood of each time step. These existing planners, however, are unable to directly benefit from the bounded treewidth owing to a constraint on the variable ordering which is necessary for correct computation of the optimal plan. We show that lifting such constr...

• Type: Conference
• Year: 2006
• Where: AIPS
• Authors: Jinbo Huang
• Keywords: AIPS 2006, Artificial Intelligence, Conformant Probabilistic Planning, Planning Problem, Small Planning Horizons