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In contrast to the prevailing tradition in epistemology, the focus in this book is on low-level inferences, i.e., those inferences that we are usually not consciously aware of and that we share with the cat nearby which infers that the bird which she sees picking grains from the dirt, is able to fly. Presumably, such inferences are not generated by explicit logical reasoning, but logical methods can be used to describe and analyze such inferences.
Part 1 gives a purely system-theoretic explication of belief and inference. Part 2 adds a reliabilist theory of justification for inference, with a qualitative notion of reliability being employed. Part 3 recalls and extends various systems of deductive and nonmonotonic logic and thereby explains the semantics of absolute and high reliability. In Part 4 it is proven that qualitative neural networks are able to draw justified deductive and nonmonotonic inferences on the basis of distributed representations. This is derived from a soundness/completeness theorem with regard to cognitive semantics of nonmonotonic reasoning. The appendix extends the theory both logically and ontologically, and relates it to A. Goldman's reliability account of justified belief.
Table des matières
1 Introduction.- 2 Preliminaries.- I The Explication of Monotonic and Nonmonotonic Inference.- 3 Belief.- 4 Inference.- II The Justification of Monotonic and Nonmonotonic Inference.- 5 General Remarks on Justification and Justified Belief.- 6 An Informal Account of Our Theory of Justified Inference.- 7 A Discussion of Reliability.- 8 A Theory of Justified Inference.- III The Logic of Justified Monotonic and Nonmonotonic Inference.- 9 The Semantics of Deductive and Nonmonotonic Logic.- 10 Systems of Deductive and Nonmonotonic Logic.- 11 Soundness and Completeness Results.- 12 Further Consequences for Justified Inference.- IV The Cognition of Justified Monotonic and Nonmonotonic Inference by Low-Level Agents.- 13 Introductory Remarks.- 14 Inhibition Nets as Simple Neural Networks.- 15 Interpreted Inhibition Net Agents.- 16 Cumulative-Ordered Interpreted Inh. Net Agents and the System CL.- 17 Cumulative-Ordered Interpreted Inhibition Net Agents as Ideal Agents.- 18 Inhibition Nets and Other Forms of Nonmonotonic Reasoning.- 19 Inhibition Nets and Artificial Neural Networks.- 20 Discussion.- V Appendix.- 21 Digression on States, Dispositions, Causation, Processes.- 22 Goldman's Reliability Account of Justified Belief.- 23 A Sketch of Logic Programming.- 24 Preferential Interpreted Inhibition Net Agents and the System P.- 25 Cumulative Interpreted Inhibition Net Agents and the System C.- 26 Simple Cumulative Interpreted Inhibition Net Agents and the System CM.- 27 Simple Preferential Interpreted Inhibition Net Agents and the System M.- References.
A propos de l'auteur
Hannes Leitgeb is Professor at the Departments of Philosophy and Mathematics at the University of Bristol. He is a Managing Editor of Studia Logica, an Associate Editor of Erkenntnis, a Subject Editor for the Stanford Encyclopedia of Philosophy, and an Editor of the Collected Works of Rudolf Carnap. He is the author of numerous papers and books on logic, philosophy of mathematics, philosophy of science, epistemology, and philosophy of language.
Résumé
In contrast to the prevailing tradition in epistemology, the focus in this book is on low-level inferences, i.e., those inferences that we are usually not consciously aware of and that we share with the cat nearby which infers that the bird which she sees picking grains from the dirt, is able to fly. Presumably, such inferences are not generated by explicit logical reasoning, but logical methods can be used to describe and analyze such inferences.
Part 1 gives a purely system-theoretic explication of belief and inference. Part 2 adds a reliabilist theory of justification for inference, with a qualitative notion of reliability being employed. Part 3 recalls and extends various systems of deductive and nonmonotonic logic and thereby explains the semantics of absolute and high reliability. In Part 4 it is proven that qualitative neural networks are able to draw justified deductive and nonmonotonic inferences on the basis of distributed representations. This is derived from a soundness/completeness theorem with regard to cognitive semantics of nonmonotonic reasoning. The appendix extends the theory both logically and ontologically, and relates it to A. Goldman's reliability account of justified belief.
Texte suppl.
From the reviews:
"The ambitious goal of the book is the formal development of realistic (in particular: dynamic and neuronal) models of the cognitive process of reasoning. … In sum, Leitgeb’s book contains highly original and far-reaching results concerning the relation between three important areas: the epistemology of belief, the logic of reasoning, and the structure and dynamics of neuronal networks. The book is recommended for every reader who is interested in at least one of these areas … ." (Gerhard Schurz, Journal for General Philosophy of Science, Vol. 38, 2007)
Commentaire
From the reviews:
"The ambitious goal of the book is the formal development of realistic (in particular: dynamic and neuronal) models of the cognitive process of reasoning. ... In sum, Leitgeb's book contains highly original and far-reaching results concerning the relation between three important areas: the epistemology of belief, the logic of reasoning, and the structure and dynamics of neuronal networks. The book is recommended for every reader who is interested in at least one of these areas ... ." (Gerhard Schurz, Journal for General Philosophy of Science, Vol. 38, 2007)
