In this paper, we introduce a probabilistic relational data model as the basis for developing multi-agent probabilistic reasoning systems. Since our model subsumes the traditional...
Behavioral research suggests that human learning in some multi-agent systems can be predicted with surprisingly simple “foresight-free” models. The current note discusses the ...
Abstract. Even if the multi-agent paradigm has been evolving for fifteen years, the development of concrete methods for problem solving remains a major challenge. This paper focus...
We introduce Biter, a platform for the teaching and research of multiagent systems’ design. Biter implements a client for the RoboCup simulator. It provides users with the basic ...
Decision-theoretic models have become increasingly popular as a basis for solving agent and multiagent problems, due to their ability to quantify the complex uncertainty and prefe...