Fast planning through planning graph analysis
Artificial Intelligence
Temporal Planning with Mutual Exclusion Reasoning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Probabilistic Planning in the Graphplan Framework
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Solving concurrent Markov decision processes
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Planning with resources and concurrency a forward chaining approach
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Planning under continuous time and resource uncertainty: a challenge for AI
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Sequential Monte Carlo in reachability heuristics for probabilistic planning
Artificial Intelligence
The factored policy-gradient planner
Artificial Intelligence
Exploration of the robustness of plans
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Probabilistic temporal planning with uncertain durations
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Probabilistic planning via heuristic forward search and weighted model counting
Journal of Artificial Intelligence Research
Planning with durative actions in stochastic domains
Journal of Artificial Intelligence Research
HPGP: an abstraction-based framework for decision-theoretic planning
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Planning with Concurrency under Resources and Time Uncertainty
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
State agnostic planning graphs: deterministic, non-deterministic, and probabilistic planning
Artificial Intelligence
The actor's view of automated planning and acting: A position paper
Artificial Intelligence
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Planning with concurrent durative actions and probabilistic effects, or probabilistic temporal planning, is a relatively new area of research. The challenge is to replicate the success of modern temporal and probabilistic planners with domains that exhibit an interaction between time and uncertainty. We present a general framework for probabilistic temporal planning in which effects, the time at which they occur, and action durations are all probabilistic. This framework includes a search space that is designed for solving probabilistic temporal planning problems via heuristic search, an algorithm that has been tailored to work with it and an effective heuristic based on an extension of the planning graph data structure. Prottle is a planner that implements this framework, and can solve problems expressed in an extension of PDDL.