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Exception-Tolerant Hierarchical Knowledge Bases for Forward Model Learning

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autorschaft

Externe Organisationen

  • Johannes Gutenberg-Universität Mainz
  • Otto-von-Guericke-Universität Magdeburg

Details

OriginalspracheEnglisch
Seiten (von - bis)249-262
Seitenumfang14
FachzeitschriftIEEE Transactions on Games
Jahrgang13
Ausgabenummer3
Frühes Online-Datum8 Juli 2020
PublikationsstatusVeröffentlicht - Sept. 2021
Extern publiziertJa

Abstract

This article provides an overview of the recently proposed forward model approximation framework for learning games of the general video game artificial intelligence (GVGAI) framework. In contrast to other general game-playing algorithms, the proposed agent model does not need a full description of the game but can learn the game's rules by observing game state transitions. Based on hierarchical knowledge bases, the forward model can be learned and revised during game-play, improving the accuracy of the agent's state predictions over time. This allows the application of simulation-based search algorithms and belief revision techniques to previously unknown settings. We show that the proposed framework is able to quickly learn a model for dynamic environments in the context of the GVGAI framework.

ASJC Scopus Sachgebiete

Zitieren

Exception-Tolerant Hierarchical Knowledge Bases for Forward Model Learning. / Apeldoorn, Daan; Dockhorn, Alexander.
in: IEEE Transactions on Games, Jahrgang 13, Nr. 3, 09.2021, S. 249-262.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Apeldoorn D, Dockhorn A. Exception-Tolerant Hierarchical Knowledge Bases for Forward Model Learning. IEEE Transactions on Games. 2021 Sep;13(3):249-262. Epub 2020 Jul 8. doi: 10.1109/TG.2020.3008002
Apeldoorn, Daan ; Dockhorn, Alexander. / Exception-Tolerant Hierarchical Knowledge Bases for Forward Model Learning. in: IEEE Transactions on Games. 2021 ; Jahrgang 13, Nr. 3. S. 249-262.
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