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The thesis presents approaches that allow for the automated creation of knowledge bases from agent behavior learned in the context of games. The aims are twofold: (1) The creation of human-readable knowledge that can provide insights into what an agent learned, and (2) the investigation of how learning agents themselves can benefit from incorporating these approaches into their learning processes. Applications are presented, e.g., in the context of general video game playing. Moreover, an outlook on the InteKRator toolbox is provided which implements the most essential approaches in a more general context for the potential use in other domains (e.g. in medical informatics).<\/jats:p>","DOI":"10.1007\/s13218-024-00845-w","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T07:01:38Z","timestamp":1714114898000},"page":"173-177","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comprehensible Extraction of Knowledge Bases for Learning Agents in Games"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5170-5893","authenticated-orcid":false,"given":"Daan","family":"Apeldoorn","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,26]]},"reference":[{"key":"845_CR1","doi-asserted-by":"publisher","unstructured":"Apeldoorn D (2023) Comprehensible Knowledge Base Extraction for Learning Agents \u2013 Practical Challengens and Applications in Games, Dissertation at TU Dortmund University. 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