Adaptive Umweltmodellierung für kognitive Systeme in offener Welt durch dynamische Konzepte und quantitative Modellbewertung
| dc.contributor.author | Kuwertz, Achim Christian | |
| dc.date.accessioned | 2025-03-07T20:37:57Z | |
| dc.date.available | 2025-03-07T20:37:57Z | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-12-05T15:40:40Z | |
| dc.identifier | ONIX_20221205_9783731512196_10 | |
| dc.identifier | 1863-6489 | |
| dc.identifier | https://library.oapen.org/handle/20.500.12657/59837 | |
| dc.identifier.uri | https://doab-dev.siscern.org/handle/20.500.12854/165999 | |
| dc.description.abstract | In this work, an approach for adaptive world modeling is proposed. World models for cognitive systems often employ predefined domain models, which may become insufficient when encountering unforeseen entities. The presented approach addresses an adaptive extension of such domain models, considering the relevance of proposed model adaptations. As a basis, a quantitative model evaluation is devised, rating the ability of a domain model to represent the currently observed environment state. | |
| dc.language | German | |
| dc.relation.ispartofseries | Karlsruher Schriften zur Anthropomatik | |
| dc.rights | open access | |
| dc.subject.classification | thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists | |
| dc.subject.other | Umweltmodellierung | |
| dc.subject.other | quantitative Modellbewertung | |
| dc.subject.other | probabilistische Informationsverarbeitung | |
| dc.subject.other | Konzeptlernen | |
| dc.subject.other | kognitive Systeme | |
| dc.subject.other | world modeling | |
| dc.subject.other | quantitative model evaluation | |
| dc.subject.other | concept learning | |
| dc.subject.other | probabilistic information processing | |
| dc.subject.other | cognitive systems | |
| dc.title | Adaptive Umweltmodellierung für kognitive Systeme in offener Welt durch dynamische Konzepte und quantitative Modellbewertung | |
| dc.type | book | |
| oapen.identifier.doi | 10.5445/KSP/1000148667 | |
| oapen.relation.isPublishedBy | 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 | |
| oapen.relation.isbn | 9783731512196 | |
| oapen.collection | AG Universitätsverlage | |
| oapen.imprint | KIT Scientific Publishing | |
| oapen.pages | 440 | |
| oapen.place.publication | Karlsruhe | |
| peerreview.review.type | Full text | |
| peerreview.anonymity | All identities known | |
| peerreview.reviewer.type | Editorial board member | |
| peerreview.reviewer.type | External peer reviewer | |
| peerreview.review.stage | Pre-publication | |
| peerreview.open.review | No | |
| peerreview.publish.responsibility | Books or series editor | |
| peerreview.id | 51a542ec-eaeb-47c2-861d-6022e981a97a | |
| dc.seriesnumber | 57 | |
| dc.abstractotherlanguage | In this work, an approach for adaptive world modeling is proposed. World models for cognitive systems often employ predefined domain models, which may become insufficient when encountering unforeseen entities. The presented approach addresses an adaptive extension of such domain models, considering the relevance of proposed model adaptations. As a basis, a quantitative model evaluation is devised, rating the ability of a domain model to represent the currently observed environment state. | |
| peerreview.title | Dissertations in Series (Dissertationen in Schriftenreihe) |
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