Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Near-Optimal Reinforcement Learning in Polynomial Time
Machine Learning
Probabilistic planning via determinization in hindsight
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Conformant planning via symbolic model checking
Journal of Artificial Intelligence Research
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Compiling uncertainty away in conformant planning problems with bounded width
Journal of Artificial Intelligence Research
Conformant planning via heuristic forward search: A new approach
Artificial Intelligence
A translation-based approach to contingent planning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
DiscoverHistory: understanding the past in planning and execution
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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Replanning via determinization is a recent, popular approach for online planning in MDPs. In this paper we adapt this idea to classical, nonstochastic domains with partial information and sensing actions. At each step we generate a candidate plan which solves a classical planning problem induced by the original problem. We execute this plan as long as it is safe to do so. When this is no longer the case, we replan. The classical planning problem we generate is based on the T0 translation, in which the classical state captures the knowledge state of the agent. We overcome the non-determinism in sensing actions, and the large domain size introduced by T0 by using state sampling. Our planner also employs a novel, lazy, regression-based method for querying the belief state.