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  1. Comprehensive knowledge base extraction for learning agents
    practical challenges and applications in games
    Autor*in: Apeldoorn, Daan
    Erschienen: 2023
    Verlag:  Verlag Mainz, Aachen

    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... mehr

    Standort:
    Freie Universität Berlin, Universitätsbibliothek
    Fernleihe:
    uneingeschränkte Fernleihe, Kopie und Ausleihe

     

    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)

     
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    Quelle: Philologische Bibliothek, FU Berlin
    Sprache: Englisch
    Medientyp: Dissertation
    ISBN: 9783958864900
    RVK Klassifikation: ST 300
    Auflage/Ausgabe: 1. Auflage
    Schriftenreihe: Wissenschafltiche Beiträge über künstliche Intelligenz ; 1
    Schlagworte: Agent <Künstliche Intelligenz>; Künstliche Intelligenz; Hierarchisches System; Wissensbasis; Computerspiel; Videospiel; Wissensextraktion; Maschinelles Lernen
    Weitere Schlagworte: HKB; Informatik Programmierung; InteKRator Toolbox; Knowledge Representation; Künstliche Intelligenz; Lernende Algorithmen; Maschinelles Lernen; Mensch-Maschinen-Interaktion; Q-Learning; Videospielprogrammierung; human-readability; knowledge bases; learned agent behavior; erste Hälfte 21. Jahrhundert (2000 bis 2050 n. Chr.); Algorithmen und Datenstrukturen; Maschinelles Lernen; Mensch-Computer-Interaktion; Spieleentwicklung und -programmierung
    Umfang: 194 Seiten, Illustrationen, Diagramme
    Bemerkung(en):

    Dissertation, Technische Universität Dortmund, 2022