Plan quality optimisation via block decomposition

Fazlul Hasan Siddiqui, Patrik Haslum

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

    9 Citations (Scopus)

    Abstract

    AI planners have to compromise between the speed of the planning process and the quality of the generated plan. Anytime planners try to balance these objectives by finding plans of better quality over time, but current anytime planners often do not make effective use of increasing runtime beyond a certain limit. We present a new method of continuing plan improvement, that works by repeatedly decomposing a given plan into subplans and optimising each subplan locally. The decomposition exploits block-structured plan deordering to identify coherent subplans, which make sense to treat as units. This approach extends the "anytime capability" of current planners - to provide continuing plan quality improvement at any time scale.

    Original languageEnglish
    Title of host publicationIJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
    Pages2387-2393
    Number of pages7
    Publication statusPublished - 2013
    Event23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China
    Duration: 3 Aug 20139 Aug 2013

    Publication series

    NameIJCAI International Joint Conference on Artificial Intelligence
    ISSN (Print)1045-0823

    Conference

    Conference23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
    Country/TerritoryChina
    CityBeijing
    Period3/08/139/08/13

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