What evolutionary game theory tells us about multiagent learning

DOI10.1016/j.artint.2007.01.004zbMath1168.68497OpenAlexW2083535091MaRDI QIDQ1028925

Simon Parsons, Karl Tuyls

Publication date: 9 July 2009

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2007.01.004

zbMATH Keywords

replicator dynamicsevolutionary game theorymultiagent learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Rationality and learning in game theory (91A26) Evolutionary games (91A22)

Related Items (7)

EAQR: a multiagent Q-learning algorithm for coordination of multiple agents ⋮ Strategic bidding in continuous double auctions ⋮ Exploration-exploitation in multi-agent learning: catastrophe theory meets game theory ⋮ On the distribution of the number of internal equilibria in random evolutionary games ⋮ A MULTIAGENT COORDINATION APPROACH TO ROBUST CONSENSUS CLUSTERING ⋮ On the expected number of internal equilibria in random evolutionary games with correlated payoff matrix ⋮ LEARNING FROM ACTIONS NOT TAKEN IN MULTIAGENT SYSTEMS

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• If multi-agent learning is the answer, what is the question?
• Learning through reinforcement and replicator dynamics
• Evolutionary Games and Population Dynamics
• The Logic of Animal Conflict
• Machine Learning: ECML 2004

This page was built for publication: What evolutionary game theory tells us about multiagent learning