This work presents a lookahead-based exploration strategy for a model-based learning agent that enables exploration of the opponent's behavior during interaction in a multi-a...
Abstract. This work merges ideas from two very different areas: Particle Swarm Optimisation and Evolutionary Game Theory. In particular, we are looking to integrate strategies from...
Agents that operate in a multi-agent system can benefit significantly from adapting to other agents while interacting with them. This work presents a general architecture for a ...
We describe a generalized Q-learning type algorithm for reinforcement learning in competitive multi-agent games. We make the observation that in a competitive setting with adaptive...
Pieter Jan't Hoen, Sander M. Bohte, Han La Poutr&e...
In this paper we address the problem of selfish behavior in ad hoc networks. We propose a strategy driven approach which aims at enforcing cooperation between network participant...
Marcin Seredynski, Pascal Bouvry, Mieczyslaw A. Kl...