The implications for philosophy and cognitive science of developments in statistical learning theory. In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni—a philosopher and an engineer—argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors—a central topic in SLT. After discussing philosophical attempts to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis (VC) dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT.

Dealing with uncertainty, moving from ignorance to knowledge, is the focus of cognitive processes. Understanding these processes and modelling, designing, and building artificial cognitive systems have long been challenging research problems. This book describes the theory and methodology of a new, scientifically well-founded general approach, and its realization in the form of intelligent systems applicable in disciplines ranging from social sciences, such as cognitive science and sociology, through natural sciences, such as life sciences and chemistry, to applied sciences, such as medicine, education, and engineering. The main subject developed in the book is cognitive reasoning investigated at three levels of abstraction: conceptual, formal, and realizational. The authors offer a model of a cognizing agent for the conceptual theory of cognitive reasoning, and they also present a logically well-founded formal cognitive reasoning framework to handle the various plausible reasoning methods. They conclude with an object model of a cognitive engine. The book is suitable for researchers, scientists, and graduate students working in the areas of artificial intelligence, mathematical logic, and philosophy.

Too often we're guided by what we last heard, by our friends' approval, by impulse—our desires, our fears. Without reflection. Without even stopping to think. ** In this book you'll learn how to reason and find your way better in life. You'll learn to see the consequences of what you and others say and do. You'll learn to see the assumptions that you and others make. You'll learn how to judge what you should believe. These are the skills we all need to make good decisions. ** Claims. Arguments. Fallacies. Analogies. Generalizing. Cause and Effect. Explanations. These are clearly set out with hundreds of examples from daily life showing how to use them. Illustrations using a cast of cartoon characters make the concepts memorable. And many exercises will help you to check your understanding. ** Truly a book for all—from high school to graduate school, from auto repair to managing a company. How to Reason will help you find a way in life that is clearer and not buffetted by the winds of nonsense and fear. ******* In Reasoning in the Sciences, you'll learn how to use your reasoning skills to understand how scientists make definitions, what an experiment is, what can go wrong with an experiment, how scientists reason with models and theories, what counts as a good explanation in science, and how to distinguish science from magic, religion, and fraud. No background in science is needed, just a healthy appetitite for learning.

In the 20th century philosophy of mathematics has to a great extent been dominated by views developed during the so-called foundational crisis in the beginning of that century. These views have primarily focused on questions pertaining to the logical structure of mathematics and questions regarding the justi?cation and consistency of mathematics. Paradigmatic in this - spect is Hilbert’s program which inherits from Frege and Russell the project to formalize all areas of ordinary mathematics and then adds the requi- ment of a proof, by epistemically privileged means (?nitistic reasoning), of the consistency of such formalized theories. While interest in modi?ed v- sions of the original foundational programs is still thriving, in the second part of the twentieth century several philosophers and historians of mat- matics have questioned whether such foundational programs could exhaust the realm of important philosophical problems to be raised about the nature of mathematics. Some have done so in open confrontation (and hostility) to the logically based analysis of mathematics which characterized the cl- sical foundational programs, while others (and many of the contributors to this book belong to this tradition) have only called for an extension of the range of questions and problems that should be raised in connection with an understanding of mathematics. The focus has turned thus to a consideration of what mathematicians are actually doing when they produce mathematics. Questions concerning concept-formation, understanding, heuristics, changes instyle of reasoning, the role of analogies and diagrams etc.

Philosophers have always recognized the value of reason, but the process of reasoning itself has only recently begun to emerge as a philosophical topic in its own right. Is reasoning a distinctive kind of mental process? If so, what is its nature? How does reasoning differ from merely freely associating thoughts? What is the relationship between reasoning about what to believe and reasoning about how to act? Is reasoning itself something you do, or something that happens to you? And what is the value of reasoning? Are there rules for good or correct reasoning and, if so, what are they like? Does good reasoning always lead to justified belief or rational action? Is there more than one way to reason correctly from your evidence? This volume comprises twelve new essays by leading researchers in the philosophy of reasoning that together address these questions and many more, and explore the connections between them.

This book constitutes the refereed proceedings of the 21st International Conference on Case-Based Reasoning Research and Development (ICCBR 2013) held in Saratoga Springs, NY, USA, in July 2013. The 17 revised full papers presented together with 9 posters were carefully reviewed and selected from 39 submissions. The presentations and posters covered a wide range of CBR topics of interest both to researchers and practitioners including case retrieval and adaptation, similarity assessment, case base maintenance, knowledge management, recommender systems, multiagent systems, textual CBR, and applications to healthcare and computer games.