We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or ...
We propose a new neural network architecture, called Simple Recurrent Temporal-Difference Networks (SR-TDNs), that learns to predict future observations in partially observable en...
In this paper we examine some problems related to capturing the structure and the topic name space of learning content in the context of Topic Map authoring. We demonstrate that t...
Collaborative learning is question-driven and open-ended by nature. Many of the techniques developed for intelligent tutoring are applicable only in more structured settings, but f...