Computing Contingent Plan Graphs using Online Planning

Author:

Maliah Shlomi1,Komarnitski Radimir1,Shani Guy1ORCID

Affiliation:

1. Software and Information Systems Engineering, Ben Gurion University of the Negev, Israel

Abstract

In contingent planning under partial observability with sensing actions, agents actively use sensing to discover meaningful facts about the world. 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. However, the translation may become very large, encumbering the task of the non-deterministic planner. 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 have an exponential width in the number of state variables, but the tree may have 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. Our method also handles non-deterministic domains, by identifying cycles in the plans. We present a set of experiments, showing our approach to scale better than state-of-the-art offline planners.

Publisher

Association for Computing Machinery (ACM)

Subject

Software,Computer Science (miscellaneous),Control and Systems Engineering

Reference46 articles.

1. P. Bertoli, A. Cimatti, M. Pistore, M. Roveri, and P. Traverso. 2001. MBP: A model based planner. In Proceedings of the IJCAI’01 Workshop on Planning under Uncertainty and Incomplete Information. Seattle.

2. Fast planning through planning graph analysis

3. Blai Bonet and Hector Geffner. 2001. GPT: A tool for planning with uncertainty and partial information. In Proceedings of the IJCAI-01 Workshop on Planning with Uncertainty and Partial Information. Citeseer, 82–87.

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