Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
LAO: a heuristic search algorithm that finds solutions with loops
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Variable Resolution Discretization in Optimal Control
Machine Learning
Rates of Convergence for Variable Resolution Schemes in Optimal Control
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Exhibiting Knowledge in Planning Problems to Minimize State Encoding Length
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Dynamic Programming
Prioritization Methods for Accelerating MDP Solvers
The Journal of Machine Learning Research
Domain-independent structured duplicate detection
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Partitioned external-memory value iteration
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Delayed duplicate detection: extended abstract
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Planning under continuous time and resource uncertainty: a challenge for AI
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Topological value iteration algorithms
Journal of Artificial Intelligence Research
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Recent progress on external-memory MDP solvers, in particular PEMVI [Dai et al., 2008], has enabled optimal solutions to large probabilistic planning problems. However, PEMVI requires a human to manually partition the MDP before the planning algorithm can be applied -- putting an added burden on the domain designer and detracting from the vision of automated planning. This paper presents a novel partitioning scheme, which automatically subdivides the state space into blocks that respect the memory constraints. Our algorithm first applies static domain analysis to identify candidates for partitioning, and then uses heuristic search to generate a 'good' partition. We evaluate the usefulness of our method in the context of PEMVI across many benchmark domains, showing that it can successfully solve extremely large problems in each domain. We also compare the performance of automatic partitioning with previously reported results using human-designed partitions. Experiments show that our algorithm generates significantly superior partitions, which speed MDP solving and also yield vast memory savings.