The study of transportability aims to identify conditions under which causal information learned from experiments can be reused in a different environment where only passive obser...
This paper investigates the problem of policy learning in multiagent environments using the stochastic game framework, which we briefly overview. We introduce two properties as de...
This paper presents a multi-agent reinforcement learning bidding approach (MARLBS) for performing multi-agent reinforcement learning. MARLBS integrates reinforcement learning, bid...
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...
In this paper, we propose a serving system consisting intelligent agents processing society information in a multi-user domain. The agents use the similarity information on the us...