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ROBOCUP
2001
Springer
96views Robotics» more  ROBOCUP 2001»
14 years 1 days ago
Strategy Learning for a Team in Adversary Environments
Team strategy acquisition is one of the most important issues of multiagent systems, especially in an adversary environment. RoboCup has been providing such an environment for AI a...
Yasutake Takahashi, Takashi Tamura, Minoru Asada
ATAL
2003
Springer
14 years 26 days ago
Discovering and exploiting synergy between hierarchical planning agents
Agents interacting in a multiagent environment not only have to be wary of interfering with each other when carrying out their tasks, but also should capitalize on opportunities f...
Jeffrey S. Cox, Edmund H. Durfee
ATAL
2009
Springer
14 years 2 months ago
Stronger CDA strategies through empirical game-theoretic analysis and reinforcement learning
We present a general methodology to automate the search for equilibrium strategies in games derived from computational experimentation. Our approach interleaves empirical game-the...
L. Julian Schvartzman, Michael P. Wellman
AMEC
2004
Springer
14 years 1 months ago
Agents' Strategies for the Dual Parallel Search in Partnership Formation Applications
In many two-sided search applications, autonomous agents can enjoy the advantage of parallel search, powered by their ability to handle an enormous amount of information, in a shor...
David Sarne, Sarit Kraus
ICMLA
2009
13 years 5 months ago
Multiagent Transfer Learning via Assignment-Based Decomposition
We describe a system that successfully transfers value function knowledge across multiple subdomains of realtime strategy games in the context of multiagent reinforcement learning....
Scott Proper, Prasad Tadepalli