Monte-Carlo Tree Search for Artificial General Intelligence in Games

Research output: ThesisDoctoral ThesisInternal

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Abstract

Research in Artificial Intelligence has shown that machines can be programmed to perform as well as, or even better than humans in specific tasks, such as playing Chess, recognizing images, or driving cars. However, humans still have the advantage of being able to switch between all these tasks autonomously, while a machine needs to be reprogrammed for each new task. The field of Artificial General Intelligence aims at creating a program that is able to perform heterogeneous tasks in different environments autonomously, similarly to humans. The ability to plan and make decisions is one of the core capabilities expected of such a program. My research presents various improvements for a strategy that searches for the best actions to perform in an unknown environment, using board games and video games as test domains. The research shows that the performance of an agent that plays many unknown (video) games can be improved by reasoning on the game rules in a computationally efficient way, using game-independent information to guide the search, and adapting and diversifying the search for each new game being played.
Original languageEnglish
Awarding Institution
  • Maastricht University
Supervisors/Advisors
  • Peeters, Ralf, Supervisor
  • Winands, Mark, Advisor
Award date13 Nov 2019
Place of PublicationMaastricht
Publisher
Print ISBNs9789463805537
DOIs
Publication statusPublished - 13 Nov 2019

Keywords

  • Monte-Carlo Tree Search
  • Artificial General Intelligence
  • General Game Playing
  • General Video Game Playing
  • Self-Adaptive Search

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