Compact Tree Encodings for Planning as QBF

Clicks: 370
ID: 65255
2018
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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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gasquet2018compactinteligencia Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gasquet, Olivier;Longin, Dominique;Maris, Fr´ed´eric;R´egnier, Pierre;Valais, Ma¨el;
Journal inteligencia artificial
Year 2018
DOI
DOI not found
URL
https://journal.iberamia.org/index.php/intartif/article/view/214
Keywords
Medicine Language. Linguistic theory. Comparative grammar social sciences (general) electronic computers. computer science ethnology. social and cultural anthropology latin america. spanish america

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