Adaptive Learning in Weighted Network Games

Network topology and equilibrium existence in weighted network congestion games

Every finite noncooperative game can be presented as a weighted network congestion game, and also as a network congestion game with player-specific costs. In the first presentation, different players may contribute differently to congestion, and in the second, they are differently (negatively) affected by it. This paper shows that the topology of the underlying (undirected two-terminal) network...

An Adaptive Learning Model in Coordination Games

In this paper, we provide a theoretical prediction of the way in which adaptive players behave in the long run in games with strict Nash equilibria. In the model, each player picks the action which has the highest assessment, which is a weighted average of past payoffs. Each player updates his assessment of the chosen action in an adaptive manner. Almost sure convergence to a Nash equilibrium i...

Learning to Play Network Games

Automated Learning in Network Games Principal Investigator

Adaptive Learning Control Network

This paper proposes a reinforcement fuzzy adaptive learning control network (RFALCON) for solving various reinforcement learning problems. The proposed RFALCON is constructed by integrating two fuzzy adaptive learning control networks (FALCON’S), each of which is a connectionist model with a feedforward multilayer network developed for the realization of a fuzzy controller. One FALCON performs ...