Outer entanglements: a general heuristic technique for improving the efficiency of planning algorithms. Issue 6 (2nd November 2018)

• Record Type: Journal Article
• Authors: Chrpa, Lukáš; Vallati, Mauro; McCluskey, Thomas Leo
• Is Part Of: Journal of experimental & theoretical artificial intelligence. Volume 30:Issue 6(2018)
• Page Start: 831
• Page End: 856
• Publication Date: 2018-11-02
• Subjects: Classical planning -- outer entanglements -- domain reformulation -- state space pruning; Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/0952813X.2018.1509377
• Languages: English
• ISSNs: 0952-813X

Abstract

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.