ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Artificial Intelligence
Encoding Planning Problems in Nonmonotonic Logic Programs
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Unifying SAT-based and graph-based planning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Planning as satisfiability: parallel plans and algorithms for plan search
Artificial Intelligence
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Symbolic Step Encodings for Object Based Communicating State Machines
FMOODS '08 Proceedings of the 10th IFIP WG 6.1 international conference on Formal Methods for Open Object-Based Distributed Systems
Heuristics for planning with SAT
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Programming for modular reconfigurable robots
Programming and Computing Software
Exploiting step semantics for efficient bounded model checking of asynchronous systems
Science of Computer Programming
Planning as satisfiability: Heuristics
Artificial Intelligence
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Planning as satisfiability is a powerful approach to solving domain independent planning problems. In this paper, we consider a relaxed semantics for plans with parallel operator application based on ∃-step semantics. Operators can be applied in parallel if there is at least one ordering in which they can be sequentially executed. Under certain conditions, we allow them to be executed simultaneously in a state s even if not all of them are applicable in s. In this case, we guarantee that they are enabled by other operators that are applied at the same time point. We formalize the semantics of parallel plans in this setting, and propose an effective translation for STRIPS problems into the propositional logic. We finally show that this relaxed semantics yields an approach to classical planning that is sometimes much more efficient than the existing SAT-based planners.