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ATAL
2010
Springer
13 years 8 months ago
Using counterfactual regret minimization to create competitive multiplayer poker agents
Games are used to evaluate and advance Multiagent and Artificial Intelligence techniques. Most of these games are deterministic with perfect information (e.g. Chess and Checkers)....
Nicholas Abou Risk, Duane Szafron
ATAL
2006
Springer
13 years 11 months ago
Learning to cooperate in multi-agent social dilemmas
In many Multi-Agent Systems (MAS), agents (even if selfinterested) need to cooperate in order to maximize their own utilities. Most of the multi-agent learning algorithms focus on...
Jose Enrique Munoz de Cote, Alessandro Lazaric, Ma...
ICML
2003
IEEE
14 years 8 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
MOBIHOC
2007
ACM
14 years 7 months ago
Distributed opportunistic scheduling for ad-hoc communications: an optimal stopping approach
We consider distributed opportunistic scheduling (DOS) in wireless ad-hoc networks, where many links contend for the same channel using random access. In such networks, distribute...
Dong Zheng, Weiyan Ge, Junshan Zhang
ICML
2001
IEEE
14 years 8 months ago
Convergence of Gradient Dynamics with a Variable Learning Rate
As multiagent environments become more prevalent we need to understand how this changes the agent-based paradigm. One aspect that is heavily affected by the presence of multiple a...
Michael H. Bowling, Manuela M. Veloso