Ben Weber Presents: Learning and Modeling Player Behavior in Games


Speaker: Ben Weber (UC Santa Cruz)
Time:    Friday 3/30, 11am – 12 noon
Place:   The Game Innovation Lab, LC 102

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Abstract:

Video games provide an excellent environment for artificial
intelligence (AI) research, because they present many real-world
properties. One of the key challenges is developing AI systems capable
of intelligently interacting with human participants. Building game AI
which engages with players invokes the following questions: what
gameplay behavior can be learned from players, and what player
behavior can be predicted based on previous gameplay interactions?  I
will present two projects which have explored these research
questions. The first project, EISBot, extracts examples from game
replays to learn how to play StarCraft from demonstration. The second
project, Madden NFL Mining, creates models of player retention by
building regression models from millions of players.  The outcomes of
these projects are techniques for building game AI which learns from
demonstration, and approaches for modeling player behavior in games.
The broader impact of this work is methods for integrating learning in
game AI, and incorporating player feedback in the game design process.

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Bio:

Ben Weber is a Ph.D. candidate working with Michael Mateas and Arnav
Jhala in the Expressive Intelligence Studio at the University of
California, Santa Cruz. His dissertation project, EISBot, incorporates
reactive planning, case-based reasoning, and machine learning to play
the real-time strategy game StarCraft. To promote research in game AI,
Ben organized the first AIIDE StarCraft AI Competition, which
attracted participants from all over the world. Ben previously worked
at Electronic Arts as a technical analyst on Madden NFL 12.