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...
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...
This paper presents the dynamics of multiple learning agents from an evolutionary game theoretic perspective. We provide replicator dynamics models for cooperative coevolutionary ...
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....
In large multiagent games, partial observability, coordination, and credit assignment persistently plague attempts to design good learning algorithms. We provide a simple and efï¬...