Abstract. In multi-agent reinforcement learning systems, it is important to share a reward among all agents. We focus on the Rationality Theorem of Profit Sharing [5] and analyze ...
We have constructed a poker classification system which makes informed betting decisions based upon three defining features extracted while playing poker: hand value, risk, and agg...
Brien Beattie, Garrett Nicolai, David Gerhard, Rob...
Dynamic changes in complex, real-time environments, such as modern video games, can violate an agent's expectations. We describe a system that responds competently to such vi...
The Multiagent Planning Architecture (MPA) is a framework for integrating diverse technologies into a system capable of solving complex planning problems. Agents within MPA share ...
The General Game Playing (GGP) problem is concerned with developing systems capable of playing many different games, even games the system has never encountered before. Successful...