Co-Learning and the Evolution of Social Activity (1993)
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BibTeX
@TECHREPORT{Shoham93co-learningand,
author = {Yoav Shoham and Moshe Tennenholtz},
title = {Co-Learning and the Evolution of Social Activity},
institution = {},
year = {1993}
}
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Abstract
We introduce the notion of co-learning, which refers to a process in which several agents simultaneously try to adapt to one another's behavior so as to produce desirable global system properties. Of particular interest are two specific co-learning settings, which relate to the emergence of conventions and the evolution of cooperation in societies, respectively. We define a basic co-learning rule, called Highest Cumulative Reward (HCR), and show that it gives rise to quite nontrivial system dynamics. In general, we are interested in the eventual convergence of the co-learning system to desirable states, as well as in the efficiency with which this convergence is attained. Our results on eventual convergence are analytic; the results on efficiency properties include analytic lower bounds as well as empirical upper bounds derived from rigorous computer simulations. 1 Introduction In multi-agent AI systems, such as multi-planner systems, it is crucial that the agents agree on certain r...
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