It is known that the complexity of the reinforcement learning algorithms, such as Q-learning, may be exponential in the number of environment’s states. It was shown, however, th...
Partially observable stochastic games (POSGs) provide a rich mathematical framework for planning under uncertainty by a group of agents. However, this modeling advantage comes wit...
General Game Playing (GGP) contest provides a research framework suitable for developing and testing AGI approaches in game domain. In this paper, we propose a new modification of...
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...
— The problem of non-cooperative power control is studied for wireless ad hoc networks supporting data services. We develop a maximum throughput based non-cooperative power contr...