Generating macro-operators by exploiting inner entanglements

Luḱǎs Chrpa, Mauro Vallati, Thomas Leo McCluskey, Diane Kitchin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


In Automated Planning, learning and exploiting additional knowledge within a domain model, in order to improve plan generation speed-up and increase the scope of problems solved, has attracted much research. Reformulation techniques such as those based on macro-operators or entanglements are very promising because they are to some extent domain model and planning engine independent. This paper aims to exploit recent work on inner entanglements, relations between pairs of planning operators and predicates encapsulating exclusivity of predicate 'achievements' or 'requirements', for generating macro-operators.We discuss conditions which are necessary for generating such macro-operators and conditions that allow removing primitive operators without compromising solvability of a given (class of) problem(s). The effectiveness of our approach will be experimentally shown on a set of well-known benchmark domains using several highperforming planning engines.

Original languageEnglish
Title of host publicationProceedings of the 10th Symposium on Abstraction, Reformulation, and Approximation, SARA 2013
Number of pages8
Publication statusPublished - 2013
Event10th Symposium on Abstraction, Reformulation, and Approximation - Leavenworth, United States
Duration: 11 Jul 201312 Jul 2013 (Link to Symposium Details )


Conference10th Symposium on Abstraction, Reformulation, and Approximation
Abbreviated titleSARA 2013
Country/TerritoryUnited States
Internet address


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