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

This chapter discusses casual and statistical models of the TETRAD program in artificial intelligence. Most linear models in the social and behavioral sciences have a causal interpretation, and often the causal claims within a statistical model are the principal concern and point of the investigation. Linear causal models include a set of equations relating the variables of the model, a set of stochastic assumptions about the probability distributions of those variables, jointly and individually, and a set of causal relationships among the variables. The strategy behind the TETRAD program is to abstract the causal structure from a statistical model, ignoring the equations and most of the statistical assumptions. The theoretical basis of the TETRAD program consists of a number of new theorems about the connection between directed graphs and statistical properties of models. The TETRAD program, however, does not do statistical estimation of parameters and performs only statistical tests for vanishing tetrad differences and vanishing partial correlations. It performs no tests of models or of the significance of parameter estimates. The use of statistical tests has a decision theoretic foundation in Neyman–Pearson theory, and the most devoted adherents of the tradition reject any theory comparisons that are not framed as hypothesis tests.

Causal and statistical models

Wegner’s tactic of describing the conscious mind as if it inhabited a Cartesian Theater in the brain is a stopgap solution that needs to be redeemed by paying off these loans of comprehension. Just how does Wegner propose to recast his points? Three quotations from Wegner’s (2002) book, each not just defensible but, I think, importantly insightful, take out Cartesian loans that are now overdue. “We can’t possibly know (let alone keep track of) the tremendous number of mechanical influences on our behavior because we inhabit an extraordinarily complicated machine” (p. 27). These machines “we inhabit” simplify things for our benefit. Who or what is this “we” that inhabits the brain? A Cartesian ghost in the machine? Surely not, in spite of first appearances. “Conscious will is particularly useful, then, as a guide to ourselves” (p. 328). Again, who or what uses this handy guide? Does one part of the brain use another part? Is it as simple as that? “Illusory or not, conscious will is the person’s guide to his or her own moral responsibility for action” (p. 341). My body is causally responsible for whatever effects emanate from it, whether it is falling down a flight of stairs, or pulling the trigger of a gun, but I, the person “inhabiting” this body, am morally responsible only for my actions. Again, who is this person and what is he doing in my body? I have defended Wegner’s tactic of temporarily indulging in these ways of speaking, and sketched a way for him to recast his points without relying on the ominous image of a Cartesian Theater in which the Self sits as Witness and Decision-Maker (Dennett 2003a; 2003b; 2003c). But I would like to see how he himself proposes to pay off these comprehension-loans, since he may have some other tricks up his sleeve. We believe in freedom of the will so that we can learn

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We believe in freedom of the will so that we can learn

Why Bayesian Confirmation Does Not Capture the Logic of Scientific Justification

This paper (1) shows that the best supported current psychological theory (Cheng, 1997) of how human subjects judge the causal power or influence of variations in presence or absence of one feature on another, given data on their covariation, tacitly uses a Bayes network which is either a noisy or gate (for causes that promote the effect) or a noisy and gate (for causes that inhibit the effect); (2) generalizes Cheng's theory to arbitrary acyclic networks of noisy or and noisy and gates; (3) gives various sufficient conditions for the estimation of the parameters in such networks when there are independent, unobserved causes; (4) distinguishes direct causal influence of one feature on another (influence along a path with one edge) from total influence (influence along all paths from one variable to another) and gives sufficient conditions for estimating each when there are unobserved causes of the outcome variable; (5) describes the relation between Cheng models and a simplified version of the "Rubin" framework for representing causal relations.

Psychological and normative theories of causal power and the probabilities of causes

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Clark Glymour

Papers

Causal and statistical models

We believe in freedom of the will so that we can learn

Why Bayesian Confirmation Does Not Capture the Logic of Scientific Justification

Psychological and normative theories of causal power and the probabilities of causes

Reasons as Causes in Bayesian Epistemology