A Counter-Example Based Approach to Probabilistic Conformant Planning

Authors

  • Xiaodi Zhang School of Computing, Australian National University
  • Alban Grastien Universit√© Paris-Saclay, CEA, List, F-91120, Palaiseau, France School of Computing, Australian National University
  • Charles Gretton School of Computing, Australian National University

DOI:

https://doi.org/10.1609/icaps.v34i1.31532

Abstract

This paper introduces a counter-example based approach for solving probabilistic conformant planning (PCP) problems. Our algorithm incrementally generates candidate plans and identifies counter-examples until it finds a plan for which the probability of success is above the specified threshold. We prove that the algorithm is sound and complete. We further propose a variation of our algorithm that uses hitting sets to accelerate the generation of candidate plans. Experimental results show that our planner is particularly suited for problems with a high probability threshold.

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Published

2024-05-30

How to Cite

Zhang, X., Grastien, A., & Gretton, C. (2024). A Counter-Example Based Approach to Probabilistic Conformant Planning. Proceedings of the International Conference on Automated Planning and Scheduling, 34(1), 689-697. https://doi.org/10.1609/icaps.v34i1.31532