Temporal planning with continuous change
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
World modeling for the dynamic construction of real-time control plans
Artificial Intelligence
Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
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
Temporal Planning with Mutual Exclusion Reasoning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Decision-Theoretic Planning with Concurrent Temporally Extended Actions
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Dynamic programming for structured continuous Markov decision problems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Branching and pruning: an optimal temporal POCL planner based on constraint programming
Artificial Intelligence
Stochastic planning with concurrent, durative actions
Stochastic planning with concurrent, durative actions
Solving concurrent Markov decision processes
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Solving generalized semi-Markov decision processes using continuous phase-type distributions
AAAI'04 Proceedings of the 19th national conference on Artifical 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
OBDD-based universal planning for synchronized agents in non-deterministic domains
Journal of Artificial Intelligence Research
AltAltp: online parallelization of plans with heuristic state search
Journal of Artificial Intelligence Research
The 3rd international planning competition: results and analysis
Journal of Artificial Intelligence Research
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Sapa: a multi-objective metric temporal planner
Journal of Artificial Intelligence Research
Taming numbers and durations in the model checking integrated planning system
Journal of Artificial Intelligence Research
mGPT: a probabilistic planner based on heuristic search
Journal of Artificial Intelligence Research
Temporal planning using subgoal partitioning and resolution in SGPlan
Journal of Artificial Intelligence Research
When is temporal planning really temporal?
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A hybridized planner for stochastic domains
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Planning with resources and concurrency a forward chaining approach
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Max-norm projections for factored MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A reinforcement learning approach to job-shop scheduling
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Planning with sharable resource constraints
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
SPUDD: stochastic planning using decision diagrams
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in 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
The factored policy-gradient planner
Artificial Intelligence
Generating plans in concurrent, probabilistic, over-subscribed domains
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Generating plans in concurrent, probabilistic, over-subscribed domains
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Human-aware task planning: An application to mobile robots
ACM Transactions on Intelligent Systems and Technology (TIST)
Topological value iteration algorithms
Journal of Artificial Intelligence Research
Automatic construction of efficient multiple battery usage policies
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Human-aware planning for robots embedded in ambient ecologies
Pervasive and Mobile Computing
Plan-based policies for efficient multiple battery load management
Journal of Artificial Intelligence Research
Generating project plans for data center transformations
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Probabilistic planning problems are typically modeled as a Markov Decision Process (MDP). MDPs, while an otherwise expressive model, allow only for sequential, non-durative actions. This poses severe restrictions in modeling and solving a real world planning problem. We extend the MDP model to incorporate-1) simultaneous action execution, 2) durative actions, and 3) stochastic durations. We develop several algorithms to combat the computational explosion introduced by these features. The key theoretical ideas used in building these algorithms are -- modeling a complex problem as an MDP in extended state/action space, pruning of irrelevant actions, sampling of relevant actions, using informed heuristics to guide the search, hybridizing different planners to achieve benefits of both, approximating the problem and replanning. Our empirical evaluation illuminates the different merits in using various algorithms, viz., optimality, empirical closeness to optimality, theoretical error bounds, and speed.