Computational modeling of human belief maintenance and decision-making processes has become increasingly important for a wide range of applications. We present a framework for mod...
Jonathan Y. Ito, David V. Pynadath, Stacy C. Marse...
Our research addresses two important problems that arise in modern large-scale distributed systems: (1) the necessity to virtualize their data flows by applying actions such as ï...
Radhika Niranjan, Ada Gavrilovska, Karsten Schwan,...
Classically, an approach to the multiagent policy learning supposed that the agents, via interactions and/or by using preliminary knowledge about the reward functions of all playe...
In this paper we combine existing work in the area of social laws with a framework for reasoning about knowledge in multi-agent systems. The unifying framework in which this is do...
Wiebe van der Hoek, Mark Roberts, Michael Wooldrid...
Motion planning for mobile agents, such as robots, acting in the physical world is a challenging task, which traditionally concerns safe obstacle avoidance. We are interested in p...