Learning by demonstration can be a powerful and natural tool for developing robot control policies. That is, instead of tedious hand-coding, a robot may learn a control policy by ...
Abstract. Successful multi-target tracking requires locating the targets and labeling their identities. This mission becomes significantly more challenging when many targets freque...
Traditional classification involves building a classifier using labeled training examples from a set of predefined classes and then applying the classifier to classify test instan...
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...
Active Learning in the classroom domain presents an interesting case for integrating physical and digital affordances. Traditional physical handouts and transparencies are giving w...