Abstract a paradigm of modern Machine Learning (ML) which uses rewards and punishments to guide the learning process. One of the central ideas of RL is learning by “direct-online...
Distributed decision making in networked systems depends critically on the timely availability of critical fresh information. Performance of networked systems, from the perspective...
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...
In the effort to understand the algorithmic limitations of computing by a swarm of robots, the research has focused on the minimal capabilities that allow a problem to be solved. ...
Paola Flocchini, David Ilcinkas, Andrzej Pelc, Nic...
An important aspect of mechanism design in social choice protocols and multiagent systems is to discourage insincere and manipulative behaviour. We examine the computational compl...