Multiagent environments are often highly dynamic and only partially observable which makes deliberative action planning computationally hard. In many such environments, however, a...
In our research we study rational agents which learn how to choose the best conditional, partial plan in any situation. The agent uses an incomplete symbolic inference engine, emp...
In recent years there has been a great deal of interest in "modular reinforcement learning" (MRL). Typically, problems are decomposed into concurrent subgoals, allowing ...
Sooraj Bhat, Charles Lee Isbell Jr., Michael Matea...
We embark on an initial study of a new class of strategic (normal-form) games, so-called ranking games, in which the payoff to each agent solely depends on his position in a ranki...
— The advent of ubiquitous computing has revolutionized distributed Multi-agent systems (MAS). Consequently, there are many software projects focusing on MASs. However, its succe...