An algorithm to learn optimal actions in distributed convex repeated games is developed.
Learning is repeated because cost functions are revealed sequentially and distributed …

We derive fundamental limitations on the performances of intrinsic averaging algorithms in
open multi-agent systems, which are systems subject to random arrivals and departures of …

In this paper, we consider a widely studied consensus algorithm for processing sensor data
of dynamic agents. A new algorithm to reach a consensus given time delay constraints is …

In this paper, we deal with the problem of distributed target tracking with heterogeneous
mobile robots. We extend the static sensor network framework, presented by some of the …

In multi-agent applications it is often the case that not all information is equally valuable to
the missions, and agents are typically resource limited. Therefore it is important to ensure …

In this paper we propose and analyze a distributed algorithm for achieving globally optimal
decisions, either estimation or detection, through a self-synchronization mechanism among …

One of the most challenging phenomena that can be observed in an ensemble of interacting
agents is that of self-organization, viz. emergent, collective behaviour, also known as …

One of the key applications of physically-deployed multi-agent systems, such as mobile
robots, drones, or personal agents in human mobility scenarios, is to promote a pervasive …

The aggregation and estimation of values over 1 networks is fundamental for distributed
applications, such as 2 wireless sensor networks. Estimating the average, minimal and 3 …

Organizing a large-scale, heterogeneous network of agents into clusters based on the
agents' class is a useful preprocessing step for cooperative tasks, where agents with the …