Autoren:	Apeldoorn, Daan
Hauptberichter:	Kern-Isberner, Gabriele
Titel:	Comprehensible knowledge base extraction for learning agents : practical challenges and applications in games
Online-Publikationsdatum:	6-Nov-2023
Erscheinungsdatum:	2023
Sprache des Dokuments:	Englisch
Zusammenfassung/Abstract:	The need for artificial intelligence systems that are not only capable of mastering complicated tasks but also of explaining their decisions has massively gained attention over the last years. This also seems to offer opportunities for further interconnecting different approaches to artificial intelligence, such as machine learning and knowledge representation. This work considers the task of learning knowledge bases from agent behavior, with a focus on human-readability, comprehensibility and applications in games. In this context, it will be presented how knowledge can be organized and processed on multiple levels of abstraction, allowing for efficient reasoning and revision. It will be investigated how learning agents can benefit from incorporating the approaches into their learning processes. Examples and applications are provided, e.g., in the context of general video game playing. The most essential approaches are implemented in the InteKRator toolbox and show potential for being applied in other domains (e.g., in medical informatics).
DDC-Sachgruppe:	004 Informatik 004 Data processing
Veröffentlichende Institution:	Johannes Gutenberg-Universität Mainz
Organisationseinheit:	FB 04 Medizin Externe Einrichtungen
Veröffentlichungsort:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-9303
Version:	Published version
Publikationstyp:	Monographie
Nutzungsrechte:	Urheberrechtsschutz
Informationen zu den Nutzungsrechten:	http://rightsstatements.org/vocab/InC/1.0/
Umfang:	194 Seiten
Verlag:	Verlag Mainz
Verlagsort:	Aachen
Erscheinungsdatum:	2023
ISBN:	978-3-95886-490-0
Anmerkung:	Zugl.: Dortmund, Univ., Diss.
Enthalten in den Sammlungen:	JGU-Publikationen