2012 | OriginalPaper | Chapter
Heterogeneous Populations of Learning Agents in the Minority Game
Authors : David Catteeuw, Bernard Manderick
Published in: Adaptive and Learning Agents
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
We study how a group of adaptive agents can coordinate when competing for limited resources. A popular game theoretic model for this is the Minority Game. In this article we show that the coordination among learning agents can improve when agents use different learning parameters or even evolve their learning parameters. Better coordination leads to less resources being wasted and agents achieving higher individual performance. We also show that learning algorithms which achieve good results when all agents use that same algorithm, may be outcompeted when directly confronting other learning algorithms in the Minority Game.