Handbook of theoretical computer science (vol. B)
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
Planning as satisfiability: parallel plans and algorithms for plan search
Artificial Intelligence
Planning with first-order temporally extended goals using heuristic search
AAAI'06 Proceedings of the 21st national 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
Planning for temporally extended goals
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
A Logic Based Approach to the Static Analysis of Production Systems
RR '09 Proceedings of the 3rd International Conference on Web Reasoning and Rule Systems
Programming for modular reconfigurable robots
Programming and Computing Software
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Planning for temporally extended goals (TEGs) expressed as formulae of Linear-time Temporal Logic (LTL) is a proper generalization of classical planning, not only allowing to specify properties of a goal state but of the whole plan execution. Additionally, LTL formulae can be used to represent domain-specific control knowledge to speed up planning. In this paper we extend SATbased planning for LTL goals (akin to bounded LTL model-checking in verification) to partially ordered plans, thus significantly increasing planning efficiency compared to purely sequential SAT planning. We consider a very relaxed notion of partial ordering and show how planning for LTL goals (without the next-time operator) can be translated into a SAT problem and solved very efficiently. The results extend the practical applicability of SATbased planning to a wider class of planning problems. In addition, they could be applied to solving problems in bounded LTL model-checking more efficiently.