Improving Domain-Independent Planning via Critical Section Macro-Operators

Lukáš Chrpa, Mauro Vallati

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

11 Citations (Scopus)


Macro-operators, macros for short, are a well-known technique for enhancing performance of planning engines by providing “short-cuts” in the state space. Existing macro learning systems usually generate macros from most frequent sequences of actions in training plans. Such approach priorities frequently used sequences of actions over meaningful activities to be performed for solving planning tasks.

This paper presents a technique that, inspired by resource locking in critical sections in parallel computing, learns macros capturing activities in which a limited resource (e.g., a robotic hand) is used. In particular, such macros capture the whole activity in which the resource is “locked” (e.g., the robotic hand is holding an object) and thus “bridge” states in which the resource is locked and cannot be used. We also introduce an “aggressive” variant of our technique that removes original operators superseded by macros from the domain model. Usefulness of macros is evaluated on several stateof-the-art planners, and a wide range of benchmarks from the learning tracks of the 2008 and 2011 editions of the International Planning Competition.
Original languageEnglish
Title of host publicationProceedings of The Thirty-Third AAAI Conference on Artificial Intelligence
PublisherAAAI press
Number of pages8
ISBN (Print)9781577358091
Publication statusPublished - 17 Jul 2019
EventThirty-Third AAAI Conference on Artificial Intelligence - Hilton Hawaiian Village, Honolulu, United States
Duration: 27 Jan 20191 Feb 2019 (Link to Conference Website)


ConferenceThirty-Third AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI-19
Country/TerritoryUnited States
Internet address


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