How and why does software evolve? This question has been under study since almost 40 years ago, and it is still a subject of controversy. In the seventies, Meir M. Lehman formulat...
Abstract. An optimal probabilistic-planning algorithm solves a problem, usually modeled by a Markov decision process, by finding its optimal policy. In this paper, we study the k ...
Reinforcement learning algorithms that use eligibility traces, such as Sarsa(λ), have been empirically shown to be effective in learning good estimated-state-based policies in pa...
Decentralized partially observable Markov decision processes (Dec-POMDPs) constitute a generic and expressive framework for multiagent planning under uncertainty. However, plannin...
Frans A. Oliehoek, Shimon Whiteson, Matthijs T. J....
In this demo, we present GENIUS, a tool that facilitates research in the area of bilateral multi-issue negotiation. It implements an open architecture allowing easy development an...
Koen V. Hindriks, Catholijn M. Jonker, Sarit Kraus...