Logic with a Probability Semantics

Logic with a Probability Semantics

Author: Theodore Hailperin

Publisher: Rowman & Littlefield

Published: 2011

Total Pages: 124

ISBN-13: 1611460107

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The present study is an extension of the topic introduced in Dr. Hailperin's Sentential Probability Logic, where the usual true-false semantics for logic is replaced with one based more on probability, and where values ranging from 0 to 1 are subject to probability axioms. Moreover, as the word "sentential" in the title of that work indicates, the language there under consideration was limited to sentences constructed from atomic (not inner logical components) sentences, by use of sentential connectives ("no," "and," "or," etc.) but not including quantifiers ("for all," "there is"). An initial introduction presents an overview of the book. In chapter one, Halperin presents a summary of results from his earlier book, some of which extends into this work. It also contains a novel treatment of the problem of combining evidence: how does one combine two items of interest for a conclusion-each of which separately impart a probability for the conclusion-so as to have a probability for the conclusion basedon taking both of the two items of interest as evidence? Chapter two enlarges the Probability Logic from the first chapter in two respects: the language now includes quantifiers ("for all," and "there is") whose variables range over atomic sentences, notentities as with standard quantifier logic. (Hence its designation: ontological neutral logic.) A set of axioms for this logic is presented. A new sentential notion-the suppositional-in essence due to Thomas Bayes, is adjoined to this logic that later becomes the basis for creating a conditional probability logic. Chapter three opens with a set of four postulates for probability on ontologically neutral quantifier language. Many properties are derived and a fundamental theorem is proved, namely, for anyprobability model (assignment of probability values to all atomic sentences of the language) there will be a unique extension of the probability values to all closed sentences of the language. The chapter concludes by showing the Borel's early denumerableprobability concept (1909) can be justified by its being, in essence, close to Hailperin's probability result applied to denumerable language. The final chapter introduces the notion of conditional-probability to a language having quantifiers of the kind


Logic with a Probability Semantics

Logic with a Probability Semantics

Author: Theodore Hailperin

Publisher:

Published: 2011

Total Pages: 123

ISBN-13: 9780984416332

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The present study is an extension of the topic introduced in Dr. Hailperin's Sentential Probability Logic, where the usual true-false semantics for logic is replaced with one based more on probability, and where values ranging from 0 to 1 are subject to probability axioms. Moreover, as the word 'sentential' in the title of that work indicates, the language there under consideration was limited to sentences constructed from atomic (not inner logical components) sentences, by use of sentential connectives ('no,' 'and,' 'or,' etc.) but not including quantifiers ('for all,' 'there is'). An initial introduction presents an overview of the book. In chapter one, Halperin presents a summary of results from his earlier book, some of which extends into this work. It also contains a novel treatment of the problem of combining evidence: how does one combine two items of interest for a conclusion-each of which separately impart a probability for the conclusion-so as to have a probability for the conclusion based on taking both of the two items of interest as evidence? Chapter two enlarges the Probability Logic from the first chapter in two respects: the language now includes quantifiers ('for all,' and 'there is') whose variables range over atomic sentences, not entities as with standard quantifier logic. (Hence its designation: ontological neutral logic.) A set of axioms for this logic is presented. A new sentential notion-the suppositional-in essence due to Thomas Bayes, is adjoined to this logic that later becomes the basis for creating a conditional probability logic. Chapter three opens with a set of four postulates for probability on ontologically neutral quantifier language. Many properties are derived and a fundamental theorem is proved, namely, for any probability model (assignment of probability values to all atomic sentences of the language) there will be a unique extension of the probability values to all closed sentences of the language. The chapter concludes by showing the Borel's early denumerable probability concept (1909) can be justified by its being, in essence, close to Hailperin's probability result applied to denumerable language. The final chapter introduces the notion of conditional-probability to a language having quantifiers of the kind discussed in chapter two. A definition of probability for this type of language is defined and some of its properties characterized. The much discussed and written about Confirmation Paradox is presented and theorems involving conditional probability for this quantifier language with the conditional are derived. Using these results, Hailperin obtains a resolution of this paradox.


Foundations of Probabilistic Logic Programming

Foundations of Probabilistic Logic Programming

Author: Fabrizio Riguzzi

Publisher: CRC Press

Published: 2023-07-07

Total Pages: 548

ISBN-13: 1000923215

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Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. This book aims at providing an overview of the field with a special emphasis on languages under the Distribution Semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online. This 2nd edition aims at reporting the most exciting novelties in the field since the publication of the 1st edition. The semantics for hybrid programs with function symbols was placed on a sound footing. Probabilistic Answer Set Programming gained a lot of interest together with the studies on the complexity of inference. Algorithms for solving the MPE and MAP tasks are now available. Inference for hybrid programs has changed dramatically with the introduction of Weighted Model Integration. With respect to learning, the first approaches for neuro-symbolic integration have appeared together with algorithms for learning the structure for hybrid programs. Moreover, given the cost of learning PLPs, various works proposed language restrictions to speed up learning and improve its scaling.


Logic, Probability, and Epistemology

Logic, Probability, and Epistemology

Author: Sahotra Sarkar

Publisher: Taylor & Francis

Published: 1996

Total Pages: 420

ISBN-13: 9780815322641

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Twenty-nine collected essays represent a critical history of Shakespeare's play as text and as theater, beginning with Samuel Johnson in 1765, and ending with a review of the Royal Shakespeare Company production in 1991. The criticism centers on three aspects of the play: the love/friendship debate.


Foundations of Probabilistic Logic Programming

Foundations of Probabilistic Logic Programming

Author: Fabrizio Riguzzi

Publisher: CRC Press

Published: 2022-09-01

Total Pages: 422

ISBN-13: 100079587X

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Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertain information by means of probability theory. Probabilistic Logic Programming is at the intersection of two wider research fields: the integration of logic and probability and Probabilistic Programming.Logic enables the representation of complex relations among entities while probability theory is useful for model uncertainty over attributes and relations. Combining the two is a very active field of study.Probabilistic Programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for the inference and learning tasks are then provided automatically by the system.Probabilistic Logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds.Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. Foundations of Probabilistic Logic Programming aims at providing an overview of the field with a special emphasis on languages under the Distribution Semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods.Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online.


Logic with a Probability Semantics

Logic with a Probability Semantics

Author: Theodore Hailperin

Publisher: Lehigh University Press

Published: 2010-12-16

Total Pages: 0

ISBN-13: 9781611460117

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The book extends the development of probability logic_a logic using probability, not verity (true, false) as the basic semantic notion. The basic connectives 'not,' 'and,' and 'or' are described in depth to include quantified formulas. Also discussed is the notion of the suppositional, and resolution of the paradox of confirmation.


Probabilistic Logics and Probabilistic Networks

Probabilistic Logics and Probabilistic Networks

Author: Rolf Haenni

Publisher: Springer Science & Business Media

Published: 2010-11-19

Total Pages: 154

ISBN-13: 9400700083

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While probabilistic logics in principle might be applied to solve a range of problems, in practice they are rarely applied - perhaps because they seem disparate, complicated, and computationally intractable. This programmatic book argues that several approaches to probabilistic logic fit into a simple unifying framework in which logically complex evidence is used to associate probability intervals or probabilities with sentences. Specifically, Part I shows that there is a natural way to present a question posed in probabilistic logic, and that various inferential procedures provide semantics for that question, while Part II shows that there is the potential to develop computationally feasible methods to mesh with this framework. The book is intended for researchers in philosophy, logic, computer science and statistics. A familiarity with mathematical concepts and notation is presumed, but no advanced knowledge of logic or probability theory is required.


Existence, Truth, and Probability

Existence, Truth, and Probability

Author: Hugues Leblanc

Publisher: SUNY Press

Published: 1982-01-01

Total Pages: 482

ISBN-13: 9780873953801

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This book includes some of the most original and influential contributions to logic and the philosophy of logic during the past twenty years. It contains thirty-five essays, many of which started new trends in logic. For example, some of the essays in Part One gave birth to what is now known as free logic, and some of the essays in Part Two were among the earliest contributions to what is now known as truth-value semantics. The essays in Part Three are contributions to and improvements of already extant logics, such as intuitionistic logic, natural deduction, and the logic of sequents. Introductions to the parts of the book cover the history of the contributions and their importance. The essays have been thoroughly revised since their publication in learned journals.


Studies in Inductive Logic and Probability

Studies in Inductive Logic and Probability

Author: Rudolf Carnap

Publisher: Univ of California Press

Published: 1980-01-01

Total Pages: 312

ISBN-13: 9780520038264

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A basic system of inductive logic; An axiomatic foundation for the logic of inductive generalization; A survey of inductive systems; On the condition of partial exchangeability; Representation theorems of the de finetti type; De finetti's generalizations of excahngeability; The structure of probabilities defined on first-order languages; A subjectivit's guide to objective chance.


Logic, Probability and Science

Logic, Probability and Science

Author:

Publisher: BRILL

Published: 2022-02-22

Total Pages: 300

ISBN-13: 9004457763

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From the contents: Charles MORGAN: Canonical models and probabilistic semantics. - Francois LEPAGE: A many-valued probabilistic logic. - Piers RAWLING: The exchange paradox, finite additivity, and the principle of dominance. - Susan VINEBERG: The logical status of conditionalization and its role in confirmation. - Deborah MAYO: Science, error statistics, and arguing from error. - Mark N. LANCE: The best is the enemy of the good: Bayesian epistemology as a case study in unhelpful idealization. - Robert B. GARDNER & Michael C. WOOTEN: An application of Bayes' theorem to population genetics. - Peter D. JOHNSON, Jr.: Another look at group selection."