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A central figure in Victorian science, William Whewell (1794-1866) held professorships in Mineralogy and Moral Philosophy at Trinity College, Cambridge, before becoming Master of the college in 1841. His mathematical textbooks, such as A Treatise on Dynamics (1823), were instrumental in bringing French analytical methods into British science. This three-volume history, first published in 1837, is one of Whewell's most famous works. It provides a history of the physical sciences that culminates with the mechanics, astronomy, and chemistry of 'modern times'. Volume 1 studies Greek physics and metaphysics, attributing their failure to a method that derived its principles from the common use of language. It surveys the state of the physical sciences in the middle ages, and deals with the rise of 'formal' astronomy, based on observation rather than calculation, as exemplified by Copernicus.
Induction (Logic) --- Inductive logic --- Logic, Inductive --- Logic --- Reasoning
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A central figure in Victorian science, William Whewell held professorships in Mineralogy & Moral Philosophy at Trinity College, Cambridge, before becoming Master of the college in 1841. His mathematical textbooks, such as A Treatise on Dynamics, were instrumental in bringing French analytical methods into British science. This three-volume history, first published in 1837, is one of Whewell's most famous works. It provides a history of the physical sciences that culminates with the mechanics, astronomy, & chemistry of 'modern times'. Volume 2 focuses on the rise & development of modern mechanics in the 17th century. Whewell shows how Galileo's laws of motion exemplify a paradigmatic shift from 'formal' to 'physical' sciences - a new approach concerned with explaining causes rather than merely observing phenomena. It also discusses the implications for physical astronomy of Newton's discoveries.
Induction (Logic) --- Inductive logic --- Logic, Inductive --- Logic --- Reasoning
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Logic is a field studied mainly by researchers and students of philosophy, mathematics and computing. Inductive logic seeks to determine the extent to which the premises of an argument entail its conclusion, aiming to provide a theory of how one should reason in the face of uncertainty. It has applications to decision making and artificial intelligence, as well as how scientists should reason when not in possession of the full facts. In this work, Jon Williamson embarks on a quest to find a general, reasonable, applicable inductive logic (GRAIL), all the while examining why pioneers such as Ludwig Wittgenstein and Rudolf Carnap did not entirely succeed in this task.
Induction (Logic). --- Induction (Logic) --- Inductive logic --- Logic, Inductive --- 510.6 --- 510.6 Mathematical logic --- Mathematical logic
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A new approach to Hume's problem of induction that justifies the optimality of induction at the level of meta-induction. Hume's problem of justifying induction has been among epistemology's greatest challenges for centuries. In this book, Gerhard Schurz proposes a new approach to Hume's problem. Acknowledging the force of Hume's arguments against the possibility of a noncircular justification of the reliability of induction, Schurz demonstrates instead the possibility of a noncircular justification of the optimality of induction, or, more precisely, of meta-induction (the application of induction to competing prediction models). Drawing on discoveries in computational learning theory, Schurz demonstrates that a regret-based learning strategy, attractivity-weighted meta-induction, is predictively optimal in all possible worlds among all prediction methods accessible to the epistemic agent. Moreover, the a priori justification of meta-induction generates a noncircular a posteriori justification of object induction. Taken together, these two results provide a noncircular solution to Hume's problem. Schurz discusses the philosophical debate on the problem of induction, addressing all major attempts at a solution to Hume's problem and describing their shortcomings; presents a series of theorems, accompanied by a description of computer simulations illustrating the content of these theorems (with proofs presented in a mathematical appendix); and defends, refines, and applies core insights regarding the optimality of meta-induction, explaining applications in neighboring disciplines including forecasting sciences, cognitive science, social epistemology, and generalized evolution theory. Finally, Schurz generalizes the method of optimality-based justification to a new strategy of justification in epistemology, arguing that optimality justifications can avoid the problems of justificatory circularity and regress.
Induction (Logic) --- Hume, David, --- Inductive logic --- Logic, Inductive --- Logic --- Reasoning --- Hume, David
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Theory of knowledge --- Knowledge, Theory of --- Induction (Logic) --- Experience --- Philosophy --- Psychology --- Reality --- Pragmatism --- Inductive logic --- Logic, Inductive --- Logic --- Reasoning --- Epistemology
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Logic --- Induction (Logic) --- -Inductive logic --- Logic, Inductive --- Reasoning --- Addresses, essays, lectures --- -Addresses, essays, lectures --- Induction (Logic). --- Inductive logic
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Induction (Logic) --- Reasoning. --- Induction (Logic). --- Reasoning --- Argumentation --- Ratiocination --- Inductive logic --- Logic, Inductive --- Reason --- Thought and thinking --- Judgment (Logic) --- Logic
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In this text, John L. Pollock examines the subject of probabilistic reasoning, making general philosophical sense of objective probabilities and exploring their relationship to the problem of induction. His main claim is that the fundamental notion of probability is nomic - that is, it involves the notion of natural law, valid across all possible worlds.
Probabilities. --- Induction (Logic) --- Inductive logic --- Logic, Inductive --- Logic --- Reasoning --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk
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This book represents a selection of papers presented at the Inductive Logic Programming (ILP) workshop held at Cumberland Lodge, Great Windsor Park. The collection marks two decades since the first ILP workshop in 1991. During this period the area has developed into the main forum for work on logic-based machine learning. The chapters cover a wide variety of topics, ranging from theory and ILP implementations to state-of-the-art applications in real-world domains. The international contributors represent leaders in the field from prestigious institutions in Europe, North America and Asia.Graduate students and researchers in this field will find this book highly useful as it provides an up-to-date insight into the key sub-areas of implementation and theory of ILP. For academics and researchers in the field of artificial intelligence and natural sciences, the book demonstrates how ILP is being used in areas as diverse as the learning of game strategies, robotics, natural language understanding, query search, drug design and protein modelling.
Logic programming. --- Induction (Logic) --- Machine learning. --- Computer programming --- Learning, Machine --- Artificial intelligence --- Machine theory --- Inductive logic --- Logic, Inductive --- Logic --- Reasoning
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Without inductive reasoning, we couldn't generalize from one instance to another, derive scientific hypotheses, or predict that the sun will rise again tomorrow morning. Despite the widespread nature of inductive reasoning, books on this topic are rare. Indeed, this is the first book on the psychology of inductive reasoning in twenty years. The chapters survey recent advances in the study of inductive reasoning and address questions about how it develops, the role of knowledge in induction, how best to model people's reasoning, and how induction relates to other forms of thinking. Written by experts in philosophy, developmental science, cognitive psychology, and computational modeling, the contributions here will be of interest to a general cognitive science audience as well as to those with a more specialized interest in the study of thinking.
Reasoning (Psychology) --- Induction (Logic) --- Inductive logic --- Logic, Inductive --- Logic --- Reasoning --- Thought and thinking --- Health Sciences --- Psychiatry & Psychology
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