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We present four new reinforcement learning algorithms based on actor-critic and natural-gradient ideas, and provide their convergence proofs. Actor-critic reinforcement learning methods are online approximations to policy iteration in which the value-function parameters are estimated using temporal difference learning and the policy parameters are updated(More)
We present four new reinforcement learning algorithms based on actor–critic, natural-gradient and function-approximation ideas, and we provide their convergence proofs. Actor–critic reinforcement learning methods are online approximations to policy iteration in which the value-function parameters are estimated using temporal difference learning and the(More)
This paper focuses on the interpretation of metaphor in discourse. We build on previous work [1] in which we provide a formalization in a computationally-oriented formal semantic framework of a set of mappings that we claim are required for the interpretation of map-transcending metaphor. Such mappings are domain-independent and are identified as invariant(More)
The purpose of this study was to assess an instrument for the peer review of inpatient teaching at Mayo. The Mayo Teaching Evaluation Form (MTEF) is an instrument, based on the Stanford seven-category educational framework, which was developed for the peer review of inpatient teaching. The MTEF has 28 Likert-scaled items derived from the Stanford Faculty(More)
BACKGROUND In an era of short inpatient stays, residents may overlook relevant elements of the differential diagnosis as they try to evaluate and treat patients. However, if a resident's first principal diagnosis is wrong, the patient's appropriate evaluation and treatment may take longer, cost more, and lead to worse outcomes. A diagnostic decision support(More)
BACKGROUND There is a growing need to educate physicians about complementary and alternative medicine (CAM). Few introductory courses in CAM have been described. PURPOSE To develop and evaluate an introductory course in CAM for medical students and residents. METHOD We conducted a controlled study evaluating a case-based, Web-based course in CAM, making(More)
Researchers often have non-privileged access to a variety of high-performance computer (HPC) systems in different administrative domains, possibly across a wide-area network. 1 Consequently, the security infrastructure becomes an important component of an overlay metacomputer: a user-level aggregation of HPC systems. The Trellis Security Infrastructure(More)
We discuss an aspect of an affect-detection system used in edrama by intelligent conversational agents, namely affective interpretation of limited sorts of metaphorical utterance. Our system currently only deals with cases, which we found to be quite common in edrama, in which a person is compared to, or stated to be, something non-human such as an animal,(More)
We prove the convergence of four new reinforcement learning algorithms based on the actorcritic architecture, on function approximation, and on natural gradients. Reinforcement learning is a class of methods for solving Markov decision processes from sample trajectories under lack of model information. Actor-critic reinforcement learning methods are online(More)