It is well-known that acting in an individually rational manner, according to the principles of classical game theory, may lead to sub-optimal solutions in a class of problems nam...
We introduce relational temporal difference learning as an effective approach to solving multi-agent Markov decision problems with large state spaces. Our algorithm uses temporal ...
We present GAMUT1 , a suite of game generators designed for testing game-theoretic algorithms. We explain why such a generator is necessary, offer a way of visualizing relationshi...
Eugene Nudelman, Jennifer Wortman, Yoav Shoham, Ke...
Multiagent distributed resource allocation requires that agents act on limited, localized information with minimum communication overhead in order to optimize the distribution of ...
We present a novel method for automatically acquiring strategies for the double auction by combining evolutionary optimization together with a principled game-theoretic analysis. ...