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Intelligent tutoring systems that utilize Bayesian Knowledge Tracing have achieved the ability to accurately predict student performance not only within the intelligent tutoring system, but on paper post-tests outside of the system. Recent work has suggested that contextual estimation of student guessing and slipping leads to better prediction within the(More)
We present AttribIt, an approach for people to create visual content using relative semantic attributes expressed in linguistic terms. During an off-line processing step, AttribIt learns semantic attributes for design components that reflect the high-level intent people may have for creating content in a domain (e.g. adjectives such as "dangerous", "scary"(More)
Reinforcement learning is a simple, and yet, comprehensive theory of learning that simultaneously models the adaptive behavior of artificial agents, such as robots and autonomous software programs, as well as attempts to explain the emergent behavior of biological systems. It also gives rise to computational ideas that provide a powerful tool to solve(More)
Natural actor-critics form a popular class of policy search algorithms for finding locally optimal policies for Markov decision processes. In this paper we address a drawback of natural actor-critics that limits their real-world applicability—their lack of safety guarantees. We present a principled algorithm for performing natural gradient descent over a(More)
This paper presents a new approach to representation discovery in reinforcement learning (RL) using basis adaptation. We introduce a general framework for basis adaptation as nonlinear separable least-squares value function approximation based on finding Fréchet gradients of an error function using variable projection func-tionals. We then present a(More)
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