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Communication Dans Un Congrès Année : 2018

Compact Tree Encodings for Planning as QBF

Résumé

Considerable improvements in the technology and per-formance of SAT solvers has made their use possiblefor the resolution of various problems in artificial intel-ligence, and among them that of generating plans. Re-cently, promising Quantified Boolean Formula (QBF)solvers have been developed and we may expect that ina near future they become as efficient as SAT solvers.So, it is interesting to use QBF language that allows usto produce more compact encodings. We present in thisarticle a translation from STRIPS planning problemsinto quantified propositional formulas. We introducetwo new Compact Tree Encodings: CTE-EFA based onExplanatory frame axioms, and CTE-OPEN based oncausal links. Then we compare both of them to CTE-NOOP based on No-op Actions proposed in (Cashmore,Fox, and Giunchiglia 2012). In terms of execution timeover benchmark problems, CTE-EFA and CTE-OPENalways performed better than CTE-NOOP.
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Dates et versions

hal-02279383 , version 1 (05-09-2019)

Identifiants

  • HAL Id : hal-02279383 , version 1
  • OATAO : 22525

Citer

Olivier Gasquet, Dominique Longin, Frédéric Maris, Pierre Régnier, Maël Valais. Compact Tree Encodings for Planning as QBF. 13th Workshop on Constraint Satisfaction Techniques for Planningand Scheduling (COPLAS 2018 @ ICAPS 2018), Jun 2018, Delft, Netherlands. pp.21-28. ⟨hal-02279383⟩
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