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» DFA Learning of Opponent Strategies
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ICML
2003
IEEE
16 years 4 months ago
AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Oppon
A satisfactory multiagent learning algorithm should, at a minimum, learn to play optimally against stationary opponents and converge to a Nash equilibrium in self-play. The algori...
Vincent Conitzer, Tuomas Sandholm
ATAL
2010
Springer
15 years 5 months ago
High-level reinforcement learning in strategy games
Video games provide a rich testbed for artificial intelligence methods. In particular, creating automated opponents that perform well in strategy games is a difficult task. For in...
Christopher Amato, Guy Shani
ICCBR
2005
Springer
15 years 9 months ago
Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game
While several researchers have applied case-based reasoning techniques to games, only Ponsen and Spronck (2004) have addressed the challenging problem of learning to win real-time ...
David W. Aha, Matthew Molineaux, Marc J. V. Ponsen
ATAL
2011
Springer
14 years 4 months ago
Game theory-based opponent modeling in large imperfect-information games
We develop an algorithm for opponent modeling in large extensive-form games of imperfect information. It works by observing the opponent’s action frequencies and building an opp...
Sam Ganzfried, Tuomas Sandholm
124
Voted
CN
2007
106views more  CN 2007»
15 years 4 months ago
Learning DFA representations of HTTP for protecting web applications
Intrusion detection is a key technology for self-healing systems designed to prevent or manage damage caused by security threats. Protecting web server-based applications using in...
Kenneth L. Ingham, Anil Somayaji, John Burge, Step...