We are interested by contributing to stochastic problems of which the main distinction is that some tasks may create other tasks. In particular, we present a first approach which...
This article shows how rational analysis can be used to minimize learning cost for a general class of statistical learning problems. We discuss the factors that influence learning...
Distributed Constraint Optimization (DCOP) is a general framework that can model complex problems in multi-agent systems. Several current algorithms that solve general DCOP instan...
Adaptive Memetic Algorithms couple an evolutionary algorithm with a number of local search heuristics for improving the evolving solutions. They are part of a broad family of meta...
Multi-agent planning in stochastic environments can be framed formally as a decentralized Markov decision problem. Many real-life distributed problems that arise in manufacturing,...