Mutual online concept learning for multiple agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Emergence of social conventions in complex networks
Artificial Intelligence
Rationality Assumptions and Optimality of Co-learning
PRIMA '00 Proceedings of the Third Pacific Rim International Workshop on Multi-Agents: Design and Applications of Intelligent Agents
Dimensions of machine learning in design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Emergence of coordination in scale-free networks
Web Intelligence and Agent Systems
Force Versus Majority: A Comparison in Convention Emergence Efficiency
Coordination, Organizations, Institutions and Norms in Agent Systems IV
Adaptive load balancing: a study in multi-agent learning
Journal of Artificial Intelligence Research
How Much Should Agents Remember? The Role of Memory Size on Convention Emergence Efficiency
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Co-ordination in artificial agent societies: social structures and its implications for autonomous problem-solving agents
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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.