The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Conflict driven learning in a quantified Boolean Satisfiability solver
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
Learning for quantified boolean logic satisfiability
Eighteenth national conference on Artificial intelligence
Planning as satisfiability: parallel plans and algorithms for plan search
Artificial Intelligence
Distance estimates for planning in the discrete belief space
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Constructing conditional plans by a theorem-prover
Journal of Artificial Intelligence Research
Efficient belief-state AND-OR search, with application to Kriegspiel
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
Beyond CNF: A Circuit-Based QBF Solver
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Heuristics for planning with SAT
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Approximation of action theories and its application to conformant planning
Artificial Intelligence
A uniform approach for generating proofs and strategies for both true and false QBF formulas
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Planning as satisfiability: Heuristics
Artificial Intelligence
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The world is unpredictable, and acting intelligently requires anticipating possible consequences of actions that are taken. Assuming that the actions and the world are deterministic, planning can be represented in the classical propositional logic. Introducing nondeterminism (but not probabilities) or several initial states increases the complexity of the planning problem and requires the use of quantified Boolean formulae (QBF). The currently leading logic-based approaches to conditional planning use explicitly or implicitly a QBF with the prefix ∃∀∃. We present formalizations of the planning problem as QBF which have an asymptotically optimal linear size and the optimal number of quantifier alternations in the prefix: ∃∀ and ∀∃. This is in accordance with the fact that the planning problem (under the restriction to polynomial size plans) is on the second level of the polynomial hierarchy, not on the third.