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

The aim of confirmation theory is to provide a true account of the principles that guide scientific argument in so far as that argument is not, and does not purport to be, of a deductive kind. A confirmation theory should serve as a critical and explanatory instrument quite as much as do theories of deductive inference. Any successful confirmation theory should, for example, reveal the structure and fallacies, if any, in Newton’s argument for universal gravitation, in nineteenth-century arguments for and against the atomic theory, in Freud’s arguments for psychoanalytic generalizations. Where scientific judgements are widely shared, and sociological factors cannot explain their ubiquity, and analysis through the lens provided by confirmation theory reveals no good explicit arguments for the judgements, confirmation theory ought at least sometimes to suggest some good arguments that may have been lurking misperceived. Theories of deductive inference do that much for scientific reasoning in so far as that reasoning is supposed to be demonstrative. We can apply quantification theory to assess the validity of scientific arguments, and although we must almost always treat such arguments as enthymematic, the premisses we interpolate are not arbitrary; in many cases, as when the same subject-matter is under discussion, there is a common set of suppressed premisses. Again, there may be differences about the correct logical form of scientific claims; differences of this kind result in (or from) different formalizations, for example, of classical mechanics. But such differences often make no difference for the assessment of validity in actual arguments. Confirmation theory should do as well in its own domain. If it fails, then it may still be of interest for many purposes, but not for the purpose of understanding scientific reasoning.

Why I am not a Bayesian

Opinion used in Europe as an arithmetical prosthesis. In the 13th century, Leo-nard Fibonnacci introduced into Europe the Hindu-Arabic system of numbers and the arithmetic algorithms they made possible. One of the first books after the Bible printed with moveable type was an Arithmetic. Even so, the algorithms were not easy and not widely disseminated. Most 17th century tradesman could not multiply. Today, every shop assistant W hen hands are wringing, they are often wrung over the information revolution: the computer, the web, robots, the automation of manufacturing will all conspire to separate the rich and quick from the poor and slow, hurrying the trend to an informed, skilled, and employed elite living among an uninformed, unskilled, and unemployed majority. But both history and an understanding of human-machine interaction argue otherwise. Any number of forces may work towards the stratification of society, but the computer is not one of them. Computers , especially intelligent ones, are the great equalizers. Humanity has always recognized that the powers of mind are limited, and has always made devices to compensate for those limitations. Our most obvious cognitive limitation is memory, and writing is a device for storing information outside the head so that it does not have to be remembered. Instead, the human brain need only store the code for reading. As soon as it could be economically reproduced and distributed, writing became in Europe an irresistible force for equality: within three centuries after Guttenberg, modern science had been created, ecclesiastical authority had been reduced, the divine right of kings had vanished, and democratic forms of government had emerged. Calculation shows the same history. Roman enumeration methods made addition, multiplication and division impossible except for the gifted. To compensate, the abacus was turning the screws, or someone driving a car not really moving along the highway? With a power screwdriver anyone can drive the hardest screw; with a calculator, anyone can get the numbers right; with an aircraft anyone can fly to Paris; and with Deep Blue, anyone can beat the world chess champion. Cognitive prostheses undermine the exclusiveness of expertise by giving non-experts equivalent capacities. As with any good tool, the effect is to make all of us more productive , more skillful, more equal. One day last year one of our fathers, Joe Glymour, told us with mixed pleasure and anxiety that he was beginning a game of postcard chess against …

On the Other Hand ... Cognitive Prostheses

Wesley Salmon’s account of causal inference and causal explanation is, very briefly, as follows: causality is a feature of processes, a feature they have in virtue of being spatio-temporally connected and of bearing a mark or marks (that is, a property the process acquires in virtue of an interaction with another process) from one space-time region to another, later one, without benefit of subsequent interaction. (“Interaction” is not supposed to be a causal primitive: two processes interact if they occupy a common space-time region — that is, if they intersect — and satisfy a statistical condition.1) The explanation of particular occurrences is always causal explanation, but Salmon allows that the concern of science is seldom to explain particular occurrences, and more often is to explain regularities. I am unsure what form Salmon supposes causal explanations, whether of particular occurrences or of patterns, to have, and I am likewise unsure how the emphasis in his more recent essays on causal explanation and the irreducibility of causal relations to statistical relations combines with his earlier emphasis on statistical explanation as a kind of sui generis form. I will simply construe his views on these matters in what I take to be both the most clear-cut and the most generous way: there is no such thing as statistical explanation per se, merely statistical evidence for (and, perhaps, statistical aspects of) causal explanations. The causal explanation of a particular event consists in the description of any interesting fragment of the causal history leading up to the event.

Causal Inference and Causal Explanation

Recent literature in philosophy of science has addressed purported notions of explanatory virtues—‘explanatory power’, ‘unification’, and ‘coherence’. In each case, a probabilistic relation...

Probability and the Explanatory Virtues

A number of approaches to causal discovery assume that there are no hidden confounders and are designed to learn a fixed causal model from a single data set. Over the last decade, with closer cooperation across laboratories, we are able to accumulate more variables and data for analysis, while each lab may only measure a subset of them, due to technical constraints or to save time and cost. This raises a question of how to handle causal discovery from multiple data sets with non-identical variable sets, and at the same time, it would be interesting to see how more recorded variables can help to mitigate the confounding problem. In this paper, we propose a principled method to uniquely identify causal relationships over the integrated set of variables from multiple data sets, in linear, non-Gaussian cases. The proposed method also allows distribution shifts across data sets. Theoretically, we show that the causal structure over the integrated set of variables is identifiable under testable conditions. Furthermore, we present two types of approaches to parameter estimation: one is based on maximum likelihood, and the other is likelihood free and leverages generative adversarial nets to improve scalability of the estimation procedure. Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods.

/pdf/causal-discovery-from-multiple-data-sets-with-non-identical-4o40opbzti.pdf

Clark Glymour

Papers

Why I am not a Bayesian

On the Other Hand ... Cognitive Prostheses

Causal Inference and Causal Explanation

Probability and the Explanatory Virtues

Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets.