We introduce a novel case study in which a group of miniaturized robots screen an environment for undesirable agents, and destroy them. Because miniaturized robots are usually end...
Recent studies have investigated how a team of mobile sensors can cope with real world constraints, such as uncertainty in the reward functions, dynamically appearing and disappea...
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 ...
In this paper, we study a maximum likelihood estimation (MLE) approach to preference aggregation and voting when the set of alternatives has a multi-issue structure, and the voter...
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 ...