Computing Contingent Plans via Fully Observable Non-Deterministic Planning


  • Christian Muise University of Toronto
  • Vaishak Belle University of Toronto
  • Sheila McIlraith University of Toronto



contingent planning, non-deterministic planning, strong cyclic planning, FOND, conditional effects, state relevance


Planning with sensing actions under partial observability is a computationally challenging problem that is fundamental to the realization of AI tasks in areas as diverse as robotics, game playing, and diagnostic problem solving. Recent work on generating plans for partially observable domains has advocated for online planning, claiming that offline plans are often too large to generate. Here we push the envelope on this challenging problem, proposing a technique for generating conditional (aka contingent) plans offline. The key to our planner's success is the reliance on state-of-the-art techniques for fully observable non-deterministic (FOND) planning. In particular, we use an existing compilation for converting a planning problem under partial observability and sensing to a FOND planning problem. With a modified FOND planner in hand, we are able to scale beyond previous techniques for generating conditional plans with solutions that are orders of magnitude smaller than previously possible in some domains.




How to Cite

Muise, C., Belle, V., & McIlraith, S. (2014). Computing Contingent Plans via Fully Observable Non-Deterministic Planning. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).