ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Learning explanation-based search control rules for partial order planning
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Fast planning through planning graph analysis
Artificial Intelligence
The logical foundations of goal-regression planning in autonomous agents
Artificial Intelligence
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Tuning SAT Checkers for Bounded Model Checking
CAV '00 Proceedings of the 12th International Conference on Computer Aided Verification
On reasonable and forced goal orderings and their use in an agenda-driven planning algorithm
Journal of Artificial Intelligence Research
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Unifying SAT-based and graph-based planning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Incremental compilation-to-SAT procedures
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
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Temporal planning (TP) is notoriously difficult because it requires to solve a propositional STRIPS planning problem with temporal constraints. In this paper, we propose an efficient strategy for solving TP, which combines, in an innovative way, several well established and studied techniques in AI, OR and constraint programming. Our approach integrates graph planning (a well studied planning paradigm), max-SAT (a constraint optimization technique), and the Program Evaluation and Review Technique (PERT), a well established technique in OR. Our method first separates the logical and temporal constraints of a TP problem and solves it in two phases. In the first phase, we apply our new STRIPS planner to generate a parallel STRIPS plan with a minimum number of parallel steps. Our new STRIPS planner is based on a new max-SAT formulation, which leads to an effective incremental learning scheme and a goal-oriented variable selection heuristic. The new STRIPS planner can generate optimal parallel plans more efficiently than the well-known SATPLAN approach. In the second phase, we apply PERT to schedule the activities in a parallel plan to create a shortest temporal plan given the STRIPS plan. When applied to the first optimal parallel STRIPS plan, this simple strategy produces optimal temporal plans on most benchmarks we have tested. This strategy can also be applied to optimal STRIPS plans of different parallel steps in an anytime fashion to find an optimal temporal plan. Our experimental results show that the new strategy is effective and the resulting algorithm outperforms many existing temporal planners.