Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Fast planning through planning graph analysis
Artificial Intelligence
Solving very large weakly coupled Markov decision processes
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
How to dynamically merge Markov decision processes
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
LAO: a heuristic search algorithm that finds solutions with loops
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Decision-Theoretic Planning with Concurrent Temporally Extended Actions
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
AltAltp: online parallelization of plans with heuristic state search
Journal of Artificial Intelligence Research
Taming numbers and durations in the model checking integrated planning system
Journal of Artificial Intelligence Research
A reinforcement learning approach to job-shop scheduling
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Planning under continuous time and resource uncertainty: a challenge for AI
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Probabilistic temporal planning with uncertain durations
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Prottle: a probabilistic temporal planner
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Planning with durative actions in stochastic domains
Journal of Artificial Intelligence Research
A hybridized planner for stochastic domains
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Typically, Markov decision problems (MDPs) assume a single action is executed per decision epoch, but in the real world one may frequently execute certain actions in parallel. This paper explores concurrent MDPs, MDPs which allow multiple non-conflicting actions to be executed simultaneously, and presents two new algorithms. Our first approach exploits two provably sound pruning rules, and thus guarantees solution optimality. Our second technique is a fast, sampling-based algorithm, which produces c1ose-to-optimal solutions extremely quickly. Experiments show that our approaches outperform the existing algorithms producing up to two orders of magnitude speedup.