Integrating Learning in a Multi-Scale Agent
Integrating Learning in a Multi-Scale Agent（2012）
摘要
Ben Weber's dissertation focuses on the goal of building human-level artificial intelligence for real-time strategy games. It explores the following research question: what capabilities are necessary for expert RTS gameplay and how can these capabilities be integrated in a complete game playing agent? The dissertation was produced under the advisorship of Michael Mateas and Arnav Jhala in the Expressive Intelligence Studio at the University of California, Santa Cruz.
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关键词
integrating learning,santa cruz,following research question,multi-scale agent,multi-scale ai problem,human-level artificial intelligence,authoring agent,arnav jhala,resulting agent,rts gameplay,gameplay demonstration,expert rts gameplay,starcraft gameplay,complete game,michael mateas,real-time strategy game,professional gameplay,starcraft agent,expressive intelligence studio,expert starcraft gameplay,ben weber,computer science
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