Compact Tree Encodings for Planning as QBF

  • Olivier Gasquet IRIT-University Toulouse 3
  • Dominique Longin
  • Fr´ed´eric Maris
  • Pierre R´egnier
  • Ma¨el Valais

Abstract

Considerable improvements in the technology and performance of SAT solvers has made their use possible for the resolution of various problems in artificial intelligence, and among them that of generating plans. Recently, promising Quantified Boolean Formula (QBF) solvers have been developed and we may expect that in a near future they become as efficient as SAT solvers. So, it is interesting to use QBF language that allows us to produce more compact encodings. We present in this article a translation from STRIPS planning problems into quantified propositional formulas. We introduce two new Compact Tree Encodings: CTE-EFA based on Explanatory frame axioms, and CTE-OPEN based on causal links. Then we compare both of them to CTE-NOOP based on No-op Actions proposed in [Cashmore et al. 2012]. In terms of execution time over benchmark problems, CTE-EFA and CTE-OPEN always performed better than CTE-NOOP.

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Published
2018-10-01
How to Cite
GASQUET, Olivier et al. Compact Tree Encodings for Planning as QBF. Inteligencia Artificial, [S.l.], v. 21, n. 62, p. 103-113, oct. 2018. ISSN 1988-3064. Available at: <https://journal.iberamia.org/index.php/intartif/article/view/214>. Date accessed: 12 dec. 2018. doi: https://doi.org/10.4114/intartif.vol21iss62pp103-113.