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      Comprehensible Knowledge Base Extraction for Learning Agents: Practical Challenges and Applications in Games

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      SKU 9783958864900 Categories ,
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      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 approac...

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      Description

      Product ID:9783958864900
      Product Form:Paperback / softback
      Country of Manufacture:GB
      Series:Wissenschaftliche Beitrage uber kunstliche Intelligenz (scientific contributions about artificial intelligence)
      Title:Comprehensible Knowledge Base Extraction for Learning Agents
      Subtitle:Practical Challenges and Applications in Games
      Authors:Author: Dr Daan, Ph.D. Apeldoorn
      Page Count:194
      Subjects:Computer science, Computer science
      Description:Select Guide Rating
      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).

      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).


      Imprint Name:Verlag G. Mainz
      Publisher Name:Verlag G. Mainz
      Country of Publication:GB
      Publishing Date:2023-07-14

      Additional information

      Weight274 g
      Dimensions208 × 150 × 13 mm