This is the first book to collect essays from philosophers, mathematicians and computer scientists working at the exciting interface of algorithmic learning theory and the epistemology of science and inductive inference. Readable, introductory essays provide engaging surveys of different, complementary, and mutually inspiring approaches to the topic, both from a philosophical and a mathematical viewpoint.
Building upon this base, subsequent papers present novel extensions of algorithmic learning theory as well as bold, new applications to traditional issues in epistemology and the philosophy of science. The volume is vital reading for students and researchers seeking a fresh, truth-directed approach to the philosophy of science and induction, epistemology, logic, and statistics.
Author(s): Michèle Friend, Norma B. Goethe, Valentina S. Harizanov (eds.)
Series: Logic, Epistemology, and the Unity of Science, 9
Publisher: Springer
Year: 2007
Language: English
Pages: 296
Tags: Epistemology; Mathematical Logic and Formal Languages; Philosophy of Science; Algorithms; Cognitive Psychology; Philosophy
Front Matter....Pages I-XIII
Front Matter....Pages 1-1
Introduction to the Philosophy and Mathematics of Algorithmic Learning Theory....Pages 1-24
Front Matter....Pages 25-25
Inductive Inference Systems for Learning Classes of Algorithmically Generated Sets and Structures....Pages 27-54
Deduction, Induction, and beyond in Parametric Logic....Pages 55-110
How Simplicity Helps You Find the Truth without Pointing at it....Pages 111-143
Induction over the Continuum....Pages 145-154
Front Matter....Pages 155-155
Logically Reliable Inductive Inference....Pages 157-178
Some Philosophical Concerns about the Confidence in ‘Confident Learning’....Pages 179-197
How to Do Things with an Infinite Regress....Pages 199-217
Trade-Offs....Pages 219-232
Two Ways of Thinking about Induction....Pages 233-258
Between History and Logic....Pages 259-278
Back Matter....Pages 279-287
