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AutorSchattenberg, Bernddc.contributor.author
Aufnahmedatum2016-03-14T15:19:51Zdc.date.accessioned
In OPARU verfügbar seit2016-03-14T15:19:51Zdc.date.available
Jahr der Erstellung2009dc.date.created
ZusammenfassungPlanning and scheduling are well-established disciplines in the field of Artificial Intelligence. They provide flexibility, robustness, and effectiveness to complex software systems in a variety of application areas. While planning is the process of finding a course of action that achieves a goal or performs a specified task, scheduling deals with the assignment of resources and time to given activities, taking into account resource restrictions and temporal dependencies. In other words, planning focuses on reasoning about causal structures and identifying the necessary actions for achieving a specific goal; scheduling concentrates on resource consumption and production for optimizing a coherent parameter assignment of a plan. As successful these techniques clearly are, the actual demands of complex, real-world applications go far beyond the potential of these single methods, however. They require an adequate integration of these problem solving methods as well as a combination of different planning and scheduling paradigms. Particularly important are abstraction-based, hierarchical approaches because of both their expressive knowledge representation and their efficiency in finding solutions. Current state-of-the-art systems rarely address the question of method integration; isolated approaches do so only in ad hoc implementations and mostly lack a proper formal basis. This thesis presents a formal framework for plan and schedule generation based on a well-founded conceptualization of refinement planning: An abstract problem specification is transformed stepwise into a concrete, executable solution. In each refinement step, plan deficiencies identify faulty or under-developed parts of the plan, which in turn triggers the generation of transformation operators that try to resolve them. All involved entities are explicitly represented and therefore transparent to the framework. This property allows for two novel aspects of our approach: [...]dc.description.abstract
Spracheendc.language.iso
Verbreitende StelleUniversität Ulmdc.publisher
LizenzStandard (Fassung vom 01.10.2008)dc.rights
Link zum Lizenztexthttps://oparu.uni-ulm.de/xmlui/license_v2dc.rights.uri
SchlagwortFormal frameworkdc.subject
SchlagwortHybrid planningdc.subject
SchlagwortRefinement-based planningdc.subject
DDC-SachgruppeDDC 004 / Data processing & computer sciencedc.subject.ddc
LCSHPlanningdc.subject.lcsh
LCSHSchedulingdc.subject.lcsh
TitelHybrid planning and schedulingdc.title
RessourcentypDissertationdc.type
DOIhttp://dx.doi.org/10.18725/OPARU-1045dc.identifier.doi
PPN308570952dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-vts-68953dc.identifier.urn
GNDAutomatische Handlungsplanungdc.subject.gnd
FakultätFakultät für Ingenieurwissenschaften und Informatikuulm.affiliationGeneral
Datum der Freischaltung2009-07-08T22:48:55Zuulm.freischaltungVTS
Peer-Reviewneinuulm.peerReview
Signatur DruckexemplarZ: J-H 13.325; W: W-H 11.775uulm.shelfmark
DCMI MedientypTextuulm.typeDCMI
VTS-ID6895uulm.vtsID
KategoriePublikationenuulm.category


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