In this paper we report on using a relational state space in multi-agent reinforcement learning. There is growing evidence in the Reinforcement Learning research community that a r...
Tom Croonenborghs, Karl Tuyls, Jan Ramon, Maurice ...
This paper looks at the problem of data prioritization, commonly found in mobile ad-hoc networks. The proposed general solution uses a machine learning approach in order to learn ...
In reinforcement learning, an agent interacting with its environment strives to learn a policy that specifies, for each state it may encounter, what action to take. Evolutionary c...
Approximate Linear Programming (ALP) is a reinforcement learning technique with nice theoretical properties, but it often performs poorly in practice. We identify some reasons for...
New forms of tangible and spatial child computer interaction and supporting technologies can be designed to leverage the way children develop intelligence in the world. In order t...