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AuthorElkawkagy, Mohameddc.contributor.author
Date of accession2016-03-15T06:23:00Zdc.date.accessioned
Available in OPARU since2016-03-15T06:23:00Zdc.date.available
Year of creation2011dc.date.created
AbstractArtificial Intelligence planning is a key problem solving technology currently being used in a variety of applications including military campaigns, robot navigation, airplane scheduling, and human computer interaction. The generation of plans - courses of actions to achieve desired goals or perform specific tasks - is a costly process, however. Developing methods to systematically reduce the search effort and increase the performance of planning systems is thus a central concern. We have developed a novel pre-processing technique to extract knowledge from a hierarchically structured planning domain and a current problem description which is used to significantly improve planning performance. This specific landmark-technique firstly enables to prune parts of the search space by identifying tasks that are not achievable from a certain initial situation. Secondly, it is used to guide hierarchical planning processes more efficiently towards a solution of a given planning problem. Finally, the technique serves to decompose the original planning problem into a set of sub-problems each of which can then be solved separately using a multi-agent based planning approach. In this talk, we will present the hierarchical landmark technique and its exploitation. Furthermore, we will show the results of the empirical evaluation of our approach, which provides evidence of the significant performance increase gained this way.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandard (Fassung vom 01.10.2008)dc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v2dc.rights.uri
KeywordHierarchical planningdc.subject
KeywordLandmarks in state based planningdc.subject
KeywordPre-processingdc.subject
Dewey Decimal GroupDDC 004 / Data processing & computer sciencedc.subject.ddc
LCSHArtificial intelligence. Planningdc.subject.lcsh
TitleHierarchical landmarks - a means to reduce search effort in hybrid planningdc.title
Resource typeDissertationdc.type
DOIhttp://dx.doi.org/10.18725/OPARU-1765dc.identifier.doi
PPN666635250dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-vts-77142dc.identifier.urn
GNDKünstliche Intelligenzdc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften und Informatikuulm.affiliationGeneral
Date of activation2011-08-02T09:15:17Zuulm.freischaltungVTS
Peer reviewneinuulm.peerReview
Shelfmark print versionZ: J-H 14.148; W: W-H 12.612uulm.shelfmark
DCMI TypeTextuulm.typeDCMI
VTS-ID7714uulm.vtsID
CategoryPublikationenuulm.category
University Bibliographyjauulm.unibibliographie


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