Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

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Reinforcement Learning: An Introduction

From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Regression is a special case, not a special subject The problems of causal inference in regression studies are instances of the problems we have considered in the previous chapters, and the solutions are to be found there as well What is singular about regression is only that a technique so ill suited to causal inference should have found such wide employment to that purpose

Regression, Causation and Prediction

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Statistical Inference and Data Mining Statistics may have little to offer the search architectures in a data mining search, but a great deal to offer in evaluating hypotheses in the search, in evaluating the results of the search, and in applying the results.

We present evidence of cross-hybridization artifact intrinsic to spotted single-dye cDNA microarrays as a result of cDNA containing 5’-end sequences of consecutive thymidine (dT) residues. These poly(dT) tracts result from the synthesis, via oligo (dT) primed reverse transcription, of expressed sequence tags (EST) cDNA from a polyadenylated mRNA template. Analysis of gene expression data from two separate experiments involving commercially available cDNA microarrays showed that ESTs whose reported sequences contain more than 11 consecutive 5’-end dT residues appeared to be strongly co-expressed. Our results suggest that expression data from microarray sequences containing 5’ poly(dT) tracts is likely to be due to cross-hybridization rather than the actual mRNA expression signal. This indicates that existing data generated by cDNA microarrays may be unreliable and should be £ltered to remove EST sequences containing signi£cant poly(dT) residues. Ideally, manufacturers of cDNA microarrays should excise any 5’ poly(dT) tract from cDNA prior to deposition on the substrate.

Evidence of cross-hybridization artifact in expressed sequence tags (ESTs) on cDNA microarrays ⁄

Tom Mitchell states that the goal of his text Machine Learning is to present the key algorithms and theory that form the core of machine learning. Not only has Mitchell suc-ceeded in his primary goal, but he has accomplished a number of other important goals.

Ramon Lull and the Infidels

In an essay recently published in this journal, Branden Fitelson argues that a variant of Miller’s argument for the language dependence of the accuracy of predictions can be applied to Joyce’s notion of accuracy of credences formulated in terms of scoring rules, resulting in a general potential problem for Joyce’s argument for probabilism. We argue that no relevant problem of the sort Fitelson supposes arises since his main theorem and his supporting arguments presuppose the validity of nonlinear transformations of credence functions that Joyce’s theory, charitably construed, would identify as invalid on the basis of the principle of simple dominance.

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Clark Glymour

Papers

Regression, Causation and Prediction

Statistical Inference and Data Mining Statistics may have little to offer the search architectures in a data mining search, but a great deal to offer in evaluating hypotheses in the search, in evaluating the results of the search, and in applying the results.

Evidence of cross-hybridization artifact in expressed sequence tags (ESTs) on cDNA microarrays ⁄

Ramon Lull and the Infidels

What language dependence problem? a reply for joyce to fitelson on joyce