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Reinforcement learning is bedeviled by the curse of dimensionality: the number of parameters to be learned grows exponentially with the size of any compact encoding of a state. Recent attempts to combat the curse of dimensionality have turned to principled ways of exploiting temporal abstraction, where decisions are not required at each step, but rather(More)
This paper introduces a novel spectral framework for solving Markov decision processes (MDPs) by jointly learning representations and optimal policies. The major components of the framework described in this paper include: (i) A general scheme for constructing representations or basis functions by diagonalizing symmetric diffusion operators (ii) A specific(More)
Manifold alignment has been found to be useful in many areas of machine learning and data mining. In this paper we introduce a novel mani-fold alignment approach, which differs from " semi-supervised alignment " and " Procrustes alignment " in that it does not require predetermining correspondences. Our approach learns a projection that maps data instances(More)
This paper presents a detailed study of average reward reinforcement learning, an undiscounted optimality framework that is more appropriate for cyclical tasks than the much better studied discounted framework. A wide spectrum of average reward algorithms are described, ranging from synchronous dynamic programming methods to several (provably convergent)(More)
We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share(More)
It is by now well-recognised that a major impediment to developing know ledge-based systems is the knowledge acquisition bottleneck the task of building up a complete enough and correct enough knowledge base to provide high-level performance. This paper proposes a new class of knowledge-based systems designed to address this knowledge-acquisition bottleneck(More)
We evaluated the impact of a set of interventions to repair students' disengagement while solving geometry problems in a tutoring system. We present a deep analysis of how a tutor can remediate a student's disengagement and motivation with self-monitoring feedback. The analysis consists of a between-subjects analyses on students learning and on students'(More)
A large class of problems of sequential decision making under uncertainty, of which the underlying probability structure is a Markov process, can be modeled as stochastic dynamic programs (referred to, in general, as Markov decision problems or MDPs). However, the computational complexity of the classical MDP algorithms, such as value iteration and policy(More)