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dc.contributor.authorScala, E
dc.contributor.authorHaslum, P
dc.contributor.authorThiébaux, S
dc.contributor.authorRamirez, M
dc.date.accessioned2020-12-14T06:34:56Z
dc.date.available2020-12-14T06:34:56Z
dc.date.issued2020-08-01
dc.identifier.citationScala, E., Haslum, P., Thiébaux, S. & Ramirez, M. (2020). Subgoaling techniques for satisficing and optimal numeric planning. Journal of Artificial Intelligence Research, 68, pp.691-752. https://doi.org/10.1613/JAIR.1.11875.
dc.identifier.issn1076-9757
dc.identifier.urihttp://hdl.handle.net/11343/254263
dc.description.abstractThis paper studies novel subgoaling relaxations for automated planning with propositional and numeric state variables. Subgoaling relaxations address one source of complexity of the planning problem: the requirement to satisfy conditions simultaneously. The core idea is to relax this requirement by recursively decomposing conditions into atomic subgoals that are considered in isolation. Such relaxations are typically used for pruning, or as the basis for computing admissible or inadmissible heuristic estimates to guide optimal or satisficing heuristic search planners. In the last decade or so, the subgoaling principle has underpinned the design of an abundance of relaxation-based heuristics whose formulations have greatly extended the reach of classical planning. This paper extends subgoaling relaxations to support numeric state variables and numeric conditions. We provide both theoretical and practical results, with the aim of reaching a good trade-off between accuracy and computation costs within a heuristic state-space search planner. Our experimental results validate the theoretical assumptions, and indicate that subgoaling substantially improves on the state of the art in optimal and satisficing numeric planning via forward state-space search.
dc.publisherAI Access Foundation
dc.titleSubgoaling techniques for satisficing and optimal numeric planning
dc.typeJournal Article
dc.identifier.doi10.1613/JAIR.1.11875
melbourne.affiliation.departmentElectrical and Electronic Engineering
melbourne.source.titleJournal of Artificial Intelligence Research
melbourne.source.volume68
melbourne.source.pages691-752
melbourne.elementsid1467296
melbourne.openaccess.urlhttp://doi.org/10.1613/JAIR.1.11875
melbourne.openaccess.statusPublished version
melbourne.contributor.authorRamirez Javega, Miguel
dc.identifier.eissn1076-9757
melbourne.accessrightsAccess this item via the Open Access location


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