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» DFA Learning of Opponent Strategies
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ICML
2003
IEEE
14 years 6 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
13 years 6 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
13 years 11 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
12 years 5 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
CN
2007
106views more  CN 2007»
13 years 5 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...