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Learning for Classical Planning
dc.contributor.advisorBarták, Roman
dc.creatorChrpa, Lukáš
dc.date.accessioned2017-04-20T04:53:26Z
dc.date.available2017-04-20T04:53:26Z
dc.date.issued2010
dc.identifier.urihttp://hdl.handle.net/20.500.11956/24721
dc.description.abstractThis thesis is mainly about classical planning for articial intelligence (AI). In planning, we deal with searching for a sequence of actions that changes the environment from a given initial state to a goal state. Planning problems in general are ones of the hardest problems not only in the area of AI, but in the whole computer science. Even though classical planning problems do not consider many aspects from the real world, their complexity reaches EXPSPACE-completeness. Nevertheless, there exist many planning systems (not only for classical planning) that were developed in the past, mainly thanks to the International Planning Competitions (IPC). Despite the current planning systems are very advanced, we have to boost these systems with additional knowledge provided by learning. In this thesis, we focused on developing learning techniques which produce additional knowledge from the training plans and transform it back into planning domains and problems. We do not have to modify the planners. The contribution of this thesis is included in three areas. First, we provided theoretical background for plan analysis by investigating action dependencies or independencies. Second, we provided a method for generating macro-operators and removing unnecessary primitive operators. Experimental evaluation of this method...en_US
dc.languageEnglishcs_CZ
dc.language.isoen_US
dc.publisherUniverzita Karlova, Matematicko-fyzikální fakultacs_CZ
dc.titleLearning for Classical Planningen_US
dc.typerigorózní prácecs_CZ
dcterms.created2010
dcterms.dateAccepted2010-05-24
dc.description.departmentDepartment of Theoretical Computer Science and Mathematical Logicen_US
dc.description.departmentKatedra teoretické informatiky a matematické logikycs_CZ
dc.description.facultyFaculty of Mathematics and Physicsen_US
dc.description.facultyMatematicko-fyzikální fakultacs_CZ
dc.identifier.repId87944
dc.title.translatedLearning for Classical Planningcs_CZ
dc.identifier.aleph001457353
thesis.degree.nameRNDr.
thesis.degree.levelrigorózní řízenícs_CZ
thesis.degree.disciplineTeoretická informatikacs_CZ
thesis.degree.disciplineTheoretical computer scienceen_US
thesis.degree.programInformatikacs_CZ
thesis.degree.programInformaticsen_US
uk.faculty-name.csMatematicko-fyzikální fakultacs_CZ
uk.faculty-name.enFaculty of Mathematics and Physicsen_US
uk.faculty-abbr.csMFFcs_CZ
uk.degree-discipline.csTeoretická informatikacs_CZ
uk.degree-discipline.enTheoretical computer scienceen_US
uk.degree-program.csInformatikacs_CZ
uk.degree-program.enInformaticsen_US
thesis.grade.csProspělcs_CZ
thesis.grade.enPassen_US
uk.abstract.enThis thesis is mainly about classical planning for articial intelligence (AI). In planning, we deal with searching for a sequence of actions that changes the environment from a given initial state to a goal state. Planning problems in general are ones of the hardest problems not only in the area of AI, but in the whole computer science. Even though classical planning problems do not consider many aspects from the real world, their complexity reaches EXPSPACE-completeness. Nevertheless, there exist many planning systems (not only for classical planning) that were developed in the past, mainly thanks to the International Planning Competitions (IPC). Despite the current planning systems are very advanced, we have to boost these systems with additional knowledge provided by learning. In this thesis, we focused on developing learning techniques which produce additional knowledge from the training plans and transform it back into planning domains and problems. We do not have to modify the planners. The contribution of this thesis is included in three areas. First, we provided theoretical background for plan analysis by investigating action dependencies or independencies. Second, we provided a method for generating macro-operators and removing unnecessary primitive operators. Experimental evaluation of this method...en_US
uk.publication-placePrahacs_CZ
uk.grantorUniverzita Karlova, Matematicko-fyzikální fakulta, Katedra teoretické informatiky a matematické logikycs_CZ


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