Formalizing planning knowledge for hierarchical planning
Computational Intelligence
Temporal planning with continuous change
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Temporal Planning with Mutual Exclusion Reasoning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Probabilistic temporal planning with uncertain durations
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
On the compilability and expressive power of propositional planning formalisms
Journal of Artificial Intelligence Research
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
The case for durative actions: a commentary on PDDL2.1
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
VHPOP: versatile heuristic partial order planner
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Temporal planning using subgoal partitioning and resolution in SGPlan
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
A robust and fast action selection mechanism for planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Managing concurrency in temporal planning using planner-scheduler interaction
Artificial Intelligence
The factored policy-gradient planner
Artificial Intelligence
A situation-calculus semantics for an expressive fragment of PDDL
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Planning with problems requiring temporal coordination
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Planning with durative actions in stochastic domains
Journal of Artificial Intelligence Research
Temporal planning in domains with linear processes
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Linear logic as a tool for planning under temporal uncertainty
Theoretical Computer Science
A temporally expressive planner based on answer set programming with constraints: preliminary design
Logic programming, knowledge representation, and nonmonotonic reasoning
Controlling narrative time in interactive storytelling
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
COLIN: planning with continuous linear numeric change
Journal of Artificial Intelligence Research
Using satisfiability for non-optimal temporal planning
JELIA'12 Proceedings of the 13th European conference on Logics in Artificial Intelligence
Flexible execution of partial order plans with temporal constraints
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
A SAT-based approach to cost-sensitive temporally expressive planning
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Automating the evaluation of planning systems
AI Communications
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While even STRIPS planners must search for plans of unbounded length, temporal planners must also cope with the fact that actions may start at any point in time. Most temporal planners cope with this challenge by restricting action start times to a small set of decision epochs, because this enables search to be carried out in state-space and leverages powerful state-based reachability heuristics, originally developed for classical planning. Indeed, decision-epoch planners won the International Planning Competition's Temporal Planning Track in 2002, 2004 and 2006. However, decision-epoch planners have a largely unrecognized weakness: they are incomplete. In order to characterize the cause of incompleteness, we identify the notion of required concurrency, which separates expressive temporal action languages from simple ones. We show that decisionepoch planners are only complete for languages in the simpler class, and we prove that the simple class is 'equivalent' to STRIPS! Surprisingly, no problems with required concurrency have been included in the planning competitions. We conclude by designing a complete state-space temporal planning algorithm, which we hope will be able to achieve high performance by leveraging the heuristics that power decision epoch planners.