To avoid the curse of dimensionality, function approximators are used in reinforcement learning to learn value functions for individual states. In order to make better use of comp...
This paper presents a novel evolutionary approach of approximating the shape of the Pareto-optimal set of multi-objective optimization problems. The evolutionary algorithm (EA) use...
The Distributed Probabilistic Protocol (DPP) is a new, approximate algorithm for solving Distributed Constraint Satisfaction Problems (DCSPs) that exploits prior knowledge to impr...
In this paper we address the problem of coordination in multi-agent sequential decision problems with infinite statespaces. We adopt a game theoretic formalism to describe the int...
—Layered streaming is an effective solution to address the receiver heterogeneity in peer-to-peer (P2P) multimedia distribution. This paper targets a fundamental challenge in thi...