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dc.contributor.authorRintanen, Jussi
dc.contributor.editorSatinder Singh, Shaul Markovitch
dc.date.accessioned2017-12-04T03:25:54Z
dc.date.available2017-12-04T03:25:54Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10072/355071
dc.description.abstractComputation of invariants, which are approximate reachability information for state-space search problems such as AI planning, has been considered to be more scalable when using a schematic representation of actions/events rather than an instantiated/ground representation. A disadvantage of schematic algorithms, however, is their complexity, which also leads to high runtimes when the number of schematic events/actions is high. We propose algorithms that reduce the problem of finding schematic invariants to solving a smaller ground problem.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAssociation for the Advancement of Artificial Intelligence (AAAI)
dc.publisher.placeUnited States
dc.publisher.urihttps://aaai.org/Conferences/AAAI/aaai17.php
dc.relation.ispartofconferencenameAAAI-17
dc.relation.ispartofconferencetitleProceedings of the Thirty-First AAAI Conference on Artificial Intelligence
dc.relation.ispartofdatefrom2017-02-04
dc.relation.ispartofdateto2017-02-09
dc.relation.ispartoflocationSan Francisco, United States
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchcode080199
dc.titleSchematic invariants by reduction to ground invariants
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorRintanen, Jussi


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    Contains papers delivered by Griffith authors at national and international conferences.

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