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.

/pdf/reinforcement-learning-an-introduction-rzxgej9p17.pdf

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

We unify two contemporary theoretical frameworks for representing causal dependencies. Directed graphical models were introduced and developed by Kiiveri, Speed, Wermuth, Lauritzen, Pearl and others. Rubin introduced a framework for analyzing the relation between the conditional probability of Y on X and the distribution Y would have if X were forced to have a particular value. Pratt and Schlaifer have extended Rubin's analysis to offer sufficient "counterfactual" conditions for the conditional distribution of Y on Z, X= x to equal the conditional distribution of Y on Z when all units in the population are forced to have that value of X. Using two axioms for directed graphical causal models, we obtain rigorous derivations of claims given by Rubin and by Pratt and Schlaifer, and we give general characterizations in terms of causal structure-represented by directed graphs--for Pratt and Schlaifer's notions of the "observability of a law" and the "observability of a law with concomitants." Results obtained in the Rubin framework are generalized, and some relevant sampling properties of graphical causal models are obtained. 1 Correspondence: C. Glymour, Department of Philosophy, Carnegie Mellon University, Pittsburgh, Pa. 15213. E-mail cgO9@andrew.cmu.edu 1 Research for this paper was supported in part by contract number N00114-89-J1964 from the Navy Personnel Research and Development Center and the Office of Naval Research. One of the aims of an empirical study may be to predict the effects a general policy would have if put in force, or to predict relevant differences resulting from alternative policies. The interest might be in predicting the differential yield if a field is planted with one species of wheat rather than another; or the difference in number of polio cases per capita if all children are vaccinated against polio as against if none are; or the difference in recidivism rates if parolees are given $600 per month for six months as against if they are given nothing; or the reduction of lung cancer deaths in middle aged smokers if they are given help in quitting cigarette smoking; or the decline in gasoline consumption if an additional dollar tax per gallon is imposed. Such inference problems are puzzling because a policy of treatment creates a potential distribution different from the distribution sampled in observations or experiments, and alternative policies of treatment create alternative potential distributions with alternative statistics. The inference task is to move from a sample of one of these distributions, the one corresponding to passive observation or experimental manipulation, to conclusions about the distribution that would result if a policy were imposed. A further feature makes prediction especially difficult. Empirical studies are often unable to control or randomize all of the relevant variables, with the result that the dependency among variables relevant to prediction may be confounded by unmeasured common causes. In that case, the effect of a policy that manipulates one of the variables cannot be expected to be predictable from sample statistics. There are many examples of predictions whose disappointment may be in part due to confounding. The second Surgeon General's report on smoking and health (1979, p. 43) found that mortality ratios (compared to permanent non-smokers) for those who quit smoking declined with the number of years since quitting, equaling lifelong non-smokers after 15 years. Brownlee (1965), following Fisher (1959) conjectured that such decreases might at least in part be due to self-selection of quitters caused by genetic or cultural factors. The Brownlee hypothesis can be represented by a simple picture.

Prediction and Experimental Design with Graphical Causal Models

Publisher Summary Reichenbach states that an inductive logic cannot be built up entirely from logical principles independent of experience, but must develop out of the reasoning practiced and useful to the natural sciences. Inductive inference system needs to be built on some solid to guide scientific methodology. This chapter describes Reichenbach's reasons for stating the inverse approach for inductive logic. Instead of “a priori” foundation of inductive logic, Reichenbach's approach to induction is largely axiomatic. Reichenbach distinguishes deductive and mathematical logic from inductive logic. The former deals with the relations among tautologies, whereas the latter deals with truth in the sense of truth in reality. Deductive and mathematical logic are built on an axiomatic system. In contrast to the formal relations that are of interest in deductive logic, inductive logic is concerned with the determination of whether various relations among quantities are true in the world.

Hans reichenbach's probability logic

Using Gebharter’s representation, we consider aspects of the problem of discovering the structure of unmeasured submechanisms when the variables in those submechanisms have not been measured. Exploiting an early insight of Sober’s, we provide a correct algorithm for identifying latent, endogenous structure—submechanisms—for a restricted class of structures. The algorithm can be merged with other methods for discovering causal relations among unmeasured variables, and feedback relations between measured variables and unobserved causes can sometimes be learned.

/pdf/what-is-going-on-inside-the-arrows-discovering-the-hidden-4guhtummye.pdf

What Is Going on Inside the Arrows? Discovering the Hidden Springs in Causal Models.

Linear causal models are often constructed to explain statistical data in domains in which experiments cannot be performed. We can distinguish two types of causal models. A quantitative linear causal model can be represented by a set of simultaneous linear equations, distributional assumptions about the independent variables, and a graph that represents the causal connections between variables. A qualitative causal model contains only the graph that represents the causal connections between variables. Great progress has been made in recent years in finding the correct quantitative causal model, given sample covariance matrices and the correct qualitative causal model. However, relatively little work has been done on finding the correct qualitative causal model from statistical data and background knowledge. Recently, several programs (including LISREL VI and EQS) have added a feature that uses numerical algorithms on an initially specified quantitative model to search for better qualitative and quantitative models. In contrast, we have developed a program, TETRAD II, that searches for and evaluates alternative qualitative causal models with fast graph algorithms that entirely bypass parameter estimation. We discuss the two approaches to model search and compare their reliability with a simulation study of 360 data sets generated from nine different linear causal models. We also discuss how our methods can be used to do more than search for modifications of an initial model. We show how TETRAD II can construct initial models from just covariance data and background knowledge.

A qualitative approach to causal modeling

We show that if any number of variables are allowed to be simultaneously and independently randomized in any one experiment, log2(N) + 1 experiments are sufficient and in the worst case necessary to determine the causal relations among N >= 2 variables when no latent variables, no sample selection bias and no feedback cycles are present. For all K, 0 < K < 1/(2N) we provide an upper bound on the number experiments required to determine causal structure when each experiment simultaneously randomizes K variables. For large N, these bounds are significantly lower than the N - 1 bound required when each experiment randomizes at most one variable. For kmax < N/2, we show that (N/kmax-1)+N/(2kmax)log2(kmax) experiments aresufficient and in the worst case necessary. We over a conjecture as to the minimal number of experiments that are in the worst case sufficient to identify all causal relations among N observed variables that are a subset of the vertices of a DAG.

Clark Glymour

Papers

Prediction and Experimental Design with Graphical Causal Models

Hans reichenbach's probability logic

What Is Going on Inside the Arrows? Discovering the Hidden Springs in Causal Models.

A qualitative approach to causal modeling

On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables