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Induction, Algorithmic Learning Theory, and Philosophy

  • Book
  • © 2007

Overview

Editors:
  1. Michèle Friend

    1. George Washington University, Washington, U.S.A

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  2. Norma B. Goethe

    1. National University of Cordoba, Cordoba, Argentina

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  3. Valentina S. Harizanov

    1. George Washington University, Washington, U.S.A

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  • Only book to combine philosophy essays with technical essays on algorithmic learning theory
  • Invaluable for the reflective computer scientist or the mathematician/logician interested in modelling learning
  • Deepens the argument and debate about mathematically modelling learning
  • No-one with a serious interest in the philosophy of science can afford to ignore this development
  • Refracts the classical problem of induction into a spectrum of new insights and questions

Part of the book series: Logic, Epistemology, and the Unity of Science (LEUS, volume 9)

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Table of contents (11 chapters)

  1. Front Matter

    Pages I-XIII
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  2. Introduction to the Philosophy and Mathematics of Algorithmic Learning Theory

    1. Front Matter

      Pages 1-1
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    2. Introduction to the Philosophy and Mathematics of Algorithmic Learning Theory

      • Valentina S. Harizanov, Norma B. Goethe, Michèle Friend
      Pages 1-24

  3. Technical Papers

    1. Front Matter

      Pages 25-25
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    2. Inductive Inference Systems for Learning Classes of Algorithmically Generated Sets and Structures

      • Valentina S. Harizanov
      Pages 27-54

    3. Deduction, Induction, and beyond in Parametric Logic

      • Eric Martin, Arun Sharma, Frank Stephan
      Pages 55-110

    4. How Simplicity Helps You Find the Truth without Pointing at it

      • Kevin T. Kelly
      Pages 111-143

    5. Induction over the Continuum

      • Iraj Kalantari
      Pages 145-154

  4. Philosophy Papers

    1. Front Matter

      Pages 155-155
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    2. Logically Reliable Inductive Inference

      • Oliver Schulte
      Pages 157-178

    3. Some Philosophical Concerns about the Confidence in ‘Confident Learning’

      • Michèle Friend
      Pages 179-197

    4. How to Do Things with an Infinite Regress

      • Kevin T. Kelly
      Pages 199-217

    5. Trade-Offs

      • Clark Glymour
      Pages 219-232

    6. Two Ways of Thinking about Induction

      • Norma B. Goethe
      Pages 233-258

    7. Between History and Logic

      • Brendan Larvor
      Pages 259-278

  5. Back Matter

    Pages 279-287
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Keywords

About this book

The idea of the present volume emerged in 2002 from a series of talks by Frank Stephan in 2002, and John Case in 2003, on developments of algorithmic learning theory. These talks took place in the Mathematics Department at the George Washington University. Following the talks, ValentinaHarizanovandMichèleFriendraised thepossibility ofanexchange of ideas concerning algorithmic learning theory. In particular, this was to be a mutually bene?cial exchange between philosophers, mathematicians and computer scientists. Harizanov and Friend sent out invitations for contributions and invited Norma Goethe to join the editing team. The Dilthey Fellowship of the George Washington University provided resources over the summer of 2003 to enable the editors and some of the contributors to meet in Oviedo (Spain) at the 12th International Congress of Logic, Methodology and Philosophy of Science. The editing work proceeded from there. The idea behind the volume is to rekindle interdisciplinary discussion. Algorithmic learning theory has been around for nearly half a century. The immediate beginnings can be traced back to E.M. Gold’s papers: “Limiting recursion” (1965) and “Language identi?cation in the limit” (1967). However, from a logical point of view, the deeper roots of the learni- theoretic analysis go back to Carnap’s work on inductive logic (1950, 1952).

Editors and Affiliations

  • George Washington University, Washington, U.S.A

    Michèle Friend, Valentina S. Harizanov

  • National University of Cordoba, Cordoba, Argentina

    Norma B. Goethe

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