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ATAL
2008
Springer
15 years 3 days ago
Interaction-driven Markov games for decentralized multiagent planning under uncertainty
In this paper we propose interaction-driven Markov games (IDMGs), a new model for multiagent decision making under uncertainty. IDMGs aim at describing multiagent decision problem...
Matthijs T. J. Spaan, Francisco S. Melo
COR
2008
142views more  COR 2008»
14 years 10 months ago
Application of reinforcement learning to the game of Othello
Operations research and management science are often confronted with sequential decision making problems with large state spaces. Standard methods that are used for solving such c...
Nees Jan van Eck, Michiel C. van Wezel
JMLR
2008
92views more  JMLR 2008»
14 years 10 months ago
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
This paper presents the dynamics of multiple learning agents from an evolutionary game theoretic perspective. We provide replicator dynamics models for cooperative coevolutionary ...
Liviu Panait, Karl Tuyls, Sean Luke
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
15 years 1 months ago
Indirect co-evolution for understanding belief in an incomplete information dynamic game
This study aims to design a new co-evolution algorithm, Mixture Co-evolution which enables modeling of integration and composition of direct co-evolution and indirect coevolution....
Nanlin Jin
NIPS
2003
14 years 11 months ago
All learning is Local: Multi-agent Learning in Global Reward Games
In large multiagent games, partial observability, coordination, and credit assignment persistently plague attempts to design good learning algorithms. We provide a simple and efï¬...
Yu-Han Chang, Tracey Ho, Leslie Pack Kaelbling