Temporal planning with continuous change
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Taming numbers and durations in the model checking integrated planning system
Journal of Artificial Intelligence Research
Temporal planning with mutual exclusion reasoning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Planning with resources and concurrency a forward chaining approach
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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There is an increasing interest in solving temporal planning problems. Identification and propagation of mutual exclusion relations between actions can significantly enhance the efficiency of a planner. Current definitions of mutually exclusive actions severely restrict their concurrency. In this paper, we report on thirteen groups of permanently mutually exclusive PDDL 2.1, Level 3 actions. We report on sixteen types of potentially-conflicting interactions between two actions where concurrency may be maximized by adjusting starting time of one of the two actions. We discuss several examples where actions can overlap despite conflicting preconditions and/or effects. The processes executing these actions are mostly independent. We report on a new domain-rewriting technique called "baiting" in order to improve the concurrency in temporal plans. Baiting actions lure a temporal planner into improving concurrency. The technique involves splitting user-identified operators. We report on three types of baiting (standard, double and nested) and show their suitability for various types of action interactions. Baiting requires minimal modification to the planning code. Baiting does not increase the branching in search trees. Baiting does not affect the soundness and completeness of a temporal planner. Our empirical evaluation shows that the makespans of plans generated by efficient planner Sapa with baited domain are significantly lower than makespans of plans generated without baiting.