Outer Entanglements: A General Heuristic Technique for Improving the Efficiency of Planning Algorithms

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Abstract

Domain independent planning engines accept a planning task description in a language such as PDDL and return a solution plan. Performance of planning engines can be improved by gathering additional knowledge about a class of planning tasks. In this paper we present Outer Entanglements, relations between planning operators and predicates, that are used to restrict the number of operator instances. Outer Entanglements can be encoded within a planning task description, effectively reformulating it. We provide an in depth analysis and evaluation of outer entanglements illustrating the effectiveness of using them as generic heuristics for improving the efficiency of planning engines.
Original languageEnglish
Pages (from-to)831-856
Number of pages26
JournalJournal of Experimental and Theoretical Artificial Intelligence
Volume30
Issue number6
Early online date23 Aug 2018
DOIs
Publication statusPublished - 2018

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