The application of Reinforcement Learning (RL) algorithms to learn tasks for robots is often limited by the large dimension of the state space, which may make prohibitive its appli...
Andrea Bonarini, Alessandro Lazaric, Marcello Rest...
This paper addresses the problem of identifying the value of information held by a teammate on a distributed, multi-agent team. It focuses on a distributed scheduling task in whic...
In this paper, we focus on the problem of finding a periodic allocation strategy for teams of resource-bounded agents. We propose a real-time dynamic algorithm RTDA , that exploi...
We investigate the problem of atomic commit in transactional database systems built on top of Distributed Hash Tables. Therefore we present a framework for DHTs to provide strong d...
Learning Classifier Systems use evolutionary algorithms to facilitate rule- discovery, where rule fitness is traditionally payoff based and assigned under a sharing scheme. Most c...