Seminar on Computational Learning and
Adaptation
Achieving Efficient and Cognitively Plausible Learning in Backgammon
Scott Sanner
CMU, Stanford
ACT-R Research Group
Pittsburgh, PA
ssanner@cs.stanford.edu
Traditionally, computer applications to game domains have taken a
bruteforce approach, relying on sheer computational power to
overcome the complexity of the domain. Although many of these programs
have been quite successful, it is interesting to note that humans can
still perform extremely well against computer opponents in many game
domains. Thus we are compelled to ask, if no human could match the
computational power of most of these programs, are there methods for
learning and performance in game domains that more closely reflect human
cognition? In response to this question, this talk will cover an attempt
to model how humans learn and play games by developing a
Backgammon-playing algorithm based on the ACT-R cognitive architecture.
Analysis of this algorithm shows that it is efficient and commensurate
with human abilities suggesting that it provides a cognitively plausible
theory of learning in Backgammon.
Date: Thurs., Jan 18
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Time: 4:15-5:30PM
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Place: Cordura 100
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