Much work on skewed, stochastic, high dimensional, and biased datasets usually implicitly solve each problem separately. Recently, we have been approached by Texas Commission on En...
A novel region-based progressive stereo matching algorithm is presented. It combines the strengthes of previous region-based and progressive approaches. The progressive framework ...
In this paper we propose to apply hierarchical graphs to indoor navigation. The intended purpose is to guide humans in large public buildings and assist them in wayfinding. We sta...
Partially Observable Markov Decision Processes (POMDPs) have succeeded in planning domains that require balancing actions that increase an agent's knowledge and actions that ...
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 ...