Computing contingent plans using online replanning

Radimir Komarnitsky, Guy Shani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In contingent planning under partial observability with sensing actions, agents actively use sensing to discover meaningful facts about the world. For this class of problems the solution can be represented as a plan tree, branching on various possible observations. Recent successful approaches translate the partially observable contingent problem into a non-deterministic fully observable problem, and then use a planner for non-deterministic planning. While this approach has been successful in many domains, the translation may become very large, encumbering the task of the nondeterministic planner. In this paper we suggest a different approach - using an online contingent solver repeatedly to construct a plan tree. We execute the plan returned by the online solver until the next observation action, and then branch on the possible observed values, and replan for every branch independently. In many cases a plan tree can be exponential in the number of state variables, but still, the tree has a structure that allows us to compactly represent it using a directed graph. We suggest a mechanism for tailoring such a graph that reduces both the computational effort and the storage space. Furthermore, unlike recent state of the art offline planners, our approach is not bounded to a specific class of contingent problems, such as limited problem width, or simple contingent problems. We present a set of experiments, showing our approach to scale better than state of the art offline planners.

Original languageAmerican English
Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
Number of pages7
ISBN (Electronic)9781577357605
StatePublished - 16 May 2016
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: 12 Feb 201617 Feb 2016


Conference30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/TerritoryUnited States


  • Offline
  • Online
  • Plan tree
  • contingent planning

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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