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Aaron C Gaudette (Independent, aaronsee.media)
Location: Room 2009, West Hall
Date: Tuesday, March 22
Time: 1:20 pm - 2:20 pm
Pass Type: All Access Pass, Summits Pass - Get your pass now!
Viewing Experience: In-Person
Vault Recording: Video
Audience Level: Yes
Utility AI has a reputation of being difficult to control compared to alternatives like behavior trees or FSMs. Designing and tuning utility architectures is sometimes perceived as more of an art than a science. What does the value of a utility score really mean? What concrete effect does changing a utility score have? How many "apples" is an orange worth?
Intuitively, it would make sense to provide utility with a standard unit of measurement. This idea has struggled in the real world, since it is difficult to imagine how one might "measure" happiness. However, it is plausible in a game environment, where designers have perfect information.
In this session, a new "unit utility" AI is proposed. This approach challenges incumbent methods and is informed by concepts in statistics, probability theory, and reinforcement learning.
Attendees will learn how to implement meaningful utility AI architectures around a consistent system of units, how to design a coherent set of state functions that captures agent goals, and how to develop an intuition for expected agent behavior based on concrete properties of the underlying mathematics. Interactive visualizations are provided throughout the session.
This session is intended for AI programmers and designers interested in applying utility AI to their work. A basic understanding of probability theory, agent-based modeling, and AI in games is recommended. Example content and provided materials put an emphasis on sandbox simulation games.