Experimental analysis of networks of cooperative learning agents (to verify certain properties such as the system's stability) has been commonly used due to the complexity of...
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
Establishing trust amongst agents is of central importance to the development of well-functioning multi-agent systems. For example, the anonymity of transactions on the Internet c...
Learning, planning, and representing knowledge in large state t multiple levels of temporal abstraction are key, long-standing challenges for building flexible autonomous agents. ...
This paper describes an algorithm, called CQ-learning, which learns to adapt the state representation for multi-agent systems in order to coordinate with other agents. We propose ...