We introduce an approach to autonomously creating state space abstractions for an online reinforcement learning agent using a relational representation. Our approach uses a tree-b...
We introduce relational temporal difference learning as an effective approach to solving multi-agent Markov decision problems with large state spaces. Our algorithm uses temporal ...
—Rate adaptation, as a challenging issue for wireless purpose. Relatively little attention has been paid on identifying network design, has been an active research topic for year...
This paper describes the spatial aggregation language and its applications. Spatial aggregation comprises a framework and a mechanism for organizing computations around image-like...
Christopher Bailey-Kellogg, Feng Zhao, Kenneth Yip
If a mobile computing device knows how it is positioned and oriented in relation to other devices nearby, then it can provide enhanced support for multi-device and multi-user inte...
Mike Hazas, Christian Kray, Hans-Werner Gellersen,...