Abstract – Despite increasing deployment of agent technologies in several business and industry domains, user confidence in fully automated agent driven applications is noticeab...
We propose a new classification for multi-agent learning algorithms, with each league of players characterized by both their possible strategies and possible beliefs. Using this c...
We present a reinforcement learning game player that can interact with a General Game Playing system and transfer knowledge learned in one game to expedite learning in many other ...
The aim of this research is to develop an adaptive agent based model of auction scenarios commonly used in auction theory to help understand how competitors in auctions reach equil...
Abstract. We adopt the Markov chain framework to model bilateral negotiations among agents in dynamic environments and use Bayesian learning to enable them to learn an optimal stra...