mycoreobjectrosdok_disshab_0000001837rosdok_disshab_0000001837rosdokdisshabrosdok_derivate_00000381962023-08-08T10:08:33.238Z2018-01-26T09:04:06.024ZadministratoreditorDpublished1rosdok_derivate_0000038196truetruederivate_types:fulltextVolltextfulltextwf_edit_epub wf_register_epubstate:publishedstate:publishedveröffentlichtpublishedrosdok/id000020151016164823MODS updated during RosDok migration in June 2021DissertationHochschulschrift1079875972FrankKrüger1980-VerfasserInautActivity, context, and plan recognition with computational causal behavior modelsen137244754Dr.-Ing.ThomasKirsteUniversität Rostock, Fakultät für InformatikAkademischeR BetreuerIndgs136258794Dr. rer. nat.AdelindeUhrmacherUniversität Rostock, Fakultät für InformatikAkademischeR BetreuerIndgs131862758Dr.-Ing.FrankOrtmeierOtto-von-Guericke-Universität Magdeburg, Fakultät für InformatikAkademischeR BetreuerIndgs10085032-7Universität RostockFakultät für Informatik und ElektrotechnikGrad-verleihende Institutiondgg10.18453/rosdok_id00002015http://purl.uni-rostock.de/rosdok/id00002015urn:nbn:de:gbv:28-diss2018-0009-0004 InformatikFakultät für Informatik und Elektrotechnikfrei zugänglich (Open Access)Lizenz Metadaten: CC0Nutzungsrechte erteiltalle Rechte vorbehaltenUniversität RostockRostock2018monographic20162018Universitätsbibliothek RostockRostock20182018Objective of this thesis is to answer the question "how to achieve efficient sensor-based reconstruction of causal structures of human behaviour in order to provide assistance?". To answer this question, the concept of Computational Causal Behaviour Models (CCBMs) is introduced. CCBM allows the specification of human behaviour by means of preconditions and effects and employs Bayesian filtering techniques to reconstruct action sequences from noisy and ambiguous sensor data. Furthermore, a novel approximative inference algorithm – the Marginal Filter – is introduced.artificial intelligenceprobabilistic inferenceinverse planningubiquituous computingUniversitätsbibliothek Rostockhttp://purl.uni-rostock.de/rosdok/id00002015doctype:epubdoctype:epubDokumenttypDocument typedoctype:epub.dissertationdoctype:epub.dissertationDissertationdoctoral thesisdiniPublType:doctoralThesis diniPublType2022:PhDThesis XMetaDissPlusThesisLevel:thesis.doctoralinfo:eu-repo/semantics/doctoralThesisdocumentnatureOfContent:ppn_105825778natureOfContent:ppn_105825778HochschulschriftdiniPublType2022:DoctoralThesisdiniPublType2022:DoctoralThesisDissertation oder HabilitationDoctoral thesisDRIVERdiniPublType2022:PhDThesisdiniPublType2022:PhDThesisDissertationPhD thesisKDSF (Pu34)XMetaDissPlusThesisLevel:thesis.doctoralXMetaDissPlusThesisLevel:thesis.doctoralDoktorarbeitdoctoral thesisrfc5646:enrfc5646:enEnglischEnglishengengSDNB:004SDNB:004004 Informatik004 Data processing Computer sciencesinstitution:unirostockinstitution:unirostockUniversität RostockUniversity of RostockUniversität RostockUniversität RostockUni.Rostockhttp://d-nb.info/gnd/38329-6institution:unirostock.iefinstitution:unirostock.iefFakultät für Informatik und ElektrotechnikFaculty of Computer Science and Electrical EngineeringUniversität Rostock. 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Thomas KirsteDr.-Ing. Thomas Kirsterosdok_disshab_0000001837-d24777e75gnd:136258794Dr. rer. nat. Adelinde UhrmacherDr. rer. nat. Adelinde Uhrmacherrosdok_disshab_0000001837-d24777e90gnd:131862758Dr.-Ing. Frank OrtmeierDr.-Ing. Frank Ortmeierrosdok_disshab_0000001837-d24777e106gnd:10085032-7Universität Rostock Fakultät für Informatik und ElektrotechnikUniversität Rostock Fakultät für Informatik und ElektrotechnikFrank KrügerDr.-Ing. Thomas KirsteDr. rer. nat. Adelinde UhrmacherDr.-Ing. Frank OrtmeierUniversität Rostock Fakultät für Informatik und ElektrotechnikFrank KrügerDr.-Ing. Thomas KirsteDr. rer. nat. Adelinde UhrmacherDr.-Ing. Frank OrtmeierUniversität Rostock Fakultät für Informatik und ElektrotechnikFrank KrügerRostockUniversität Rostockepub.dissertation10.18453/rosdok_id00002015http://purl.uni-rostock.de/rosdok/id00002015urn:nbn:de:gbv:28-diss2018-0009-0artificial intelligenceprobabilistic inferenceinverse planningubiquituous computingObjective of this thesis is to answer the question "how to achieve efficient sensor-based reconstruction of causal structures of human behaviour in order to provide assistance?". To answer this question, the concept of Computational Causal Behaviour Models (CCBMs) is introduced. CCBM allows the specification of human behaviour by means of preconditions and effects and employs Bayesian filtering techniques to reconstruct action sequences from noisy and ambiguous sensor data. 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To answer this question, the concept of Computational Causal Behaviour Models (CCBMs) is introduced. CCBM allows the specification of human behaviour by means of preconditions and effects and employs Bayesian filtering techniques to reconstruct action sequences from noisy and ambiguous sensor data. 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Thomas KirsteDr.-Ing. Thomas Kirsterosdok_disshab_0000001837-d24777e75gnd:136258794Dr. rer. nat. Adelinde UhrmacherDr. rer. nat. Adelinde Uhrmacherrosdok_disshab_0000001837-d24777e90gnd:131862758Dr.-Ing. Frank OrtmeierDr.-Ing. Frank Ortmeierrosdok_disshab_0000001837-d24777e106gnd:10085032-7Universität Rostock Fakultät für Informatik und ElektrotechnikUniversität Rostock Fakultät für Informatik und ElektrotechnikFrank KrügerDr.-Ing. Thomas KirsteDr. rer. nat. Adelinde UhrmacherDr.-Ing. Frank OrtmeierUniversität Rostock Fakultät für Informatik und ElektrotechnikFrank KrügerDr.-Ing. Thomas KirsteDr. rer. nat. Adelinde UhrmacherDr.-Ing. Frank OrtmeierUniversität Rostock Fakultät für Informatik und ElektrotechnikFrank KrügerRostockUniversität Rostockepub.dissertation10.18453/rosdok_id00002015http://purl.uni-rostock.de/rosdok/id00002015urn:nbn:de:gbv:28-diss2018-0009-0artificial intelligenceprobabilistic inferenceinverse planningubiquituous computingObjective of this thesis is to answer the question "how to achieve efficient sensor-based reconstruction of causal structures of human behaviour in order to provide assistance?". To answer this question, the concept of Computational Causal Behaviour Models (CCBMs) is introduced. CCBM allows the specification of human behaviour by means of preconditions and effects and employs Bayesian filtering techniques to reconstruct action sequences from noisy and ambiguous sensor data. 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CCBM allows the…Frank KrügerActivity, context, and plan recognition with computational causal behavior modelsUniversitätsbibliothek Rostockhttp://purl.uni-rostock.de/rosdok/id000020151079875972FrankKrüger1980-VerfasserInaut137244754Dr.-Ing.ThomasKirsteUniversität Rostock, Fakultät für InformatikAkademischeR BetreuerIndgs136258794Dr. rer. nat.AdelindeUhrmacherUniversität Rostock, Fakultät für InformatikAkademischeR BetreuerIndgs131862758Dr.-Ing.FrankOrtmeierOtto-von-Guericke-Universität Magdeburg, Fakultät für InformatikAkademischeR BetreuerIndgs10085032-7Universität RostockFakultät für Informatik und ElektrotechnikGrad-verleihende Institutiondgg[doi]10.18453/rosdok_id00002015[purl]http://purl.uni-rostock.de/rosdok/id00002015[urn]urn:nbn:de:gbv:28-diss2018-0009-0open_access20182018epoch:21th_centuryrfc5646:endoctype:epub.dissertationaccesscondition:openaccessSDNB:004institution:unirostock.iefstate:published