Donald Gillies

Artificial Intelligence and Scientific Method

• Gebundenes Buch

Produktbeschreibung

Artificial Intelligence and Scientific Method examines the remarkable advances made in the field of AI over the past twenty years, discussing their profound implications for philosophy. Taking a clear, non-technical approach, Donald Gillies focuses on two key topics within AI: machine learning in the Turing tradition and the development of logic programming and its connection with non-monotonic logic. Demonstrating how current views on scientific method are challenged by this recent research, he goes on to suggest a new framework for the study of logic. He draws on work by such seminal thinkers as Bacon, Godel, Popper, Penrose, and Lucas to address the hotly contested question of whether computers might become intellectually superior to human beings. These topics will attract a wide readership from followers of advances in artificial intelligence, to students and scholars of the history and philosophy of science.

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Produktdetails

• Verlag: Hurst & Co.
• Seitenzahl: 190
• Erscheinungstermin: 26. September 1996
• Englisch
• Abmessung: 224mm x 145mm x 17mm
• Gewicht: 340g
• ISBN-13: 9780198751588
• ISBN-10: 0198751583
• Artikelnr.: 22050353

Autorenporträt

Donald Gillies is Professor of the Philosophy of Science and Mathematics at King's College, London. His books include An Objective Theory of Probability (1973), Revolutions in Mathematics (1992), and Philosophy of Science in the Twentieth Century (1993). He was the editor of the British Journal for the Philosophy of Science from 1982 to 1985.

Inhaltsangabe

* Preface
* Acknowledgements
* Chapter 1 The Inductivist Controversy, or Bacon versus Popper
* 1.1: Bacon's Inductivism
* 1.2: Popper's Falsificationism
* 1.3: Kepler's Discovery of the Laws of Planetary Motion
* 1.4: The Discovery of the Sulphonamide Drugs
* Chapter 2 Machine Learning in the Turing Tradition
* 2.1: The Turing Tradition
* 2.2: The Practical Problem: Expert Systems and Feigenbaum's
Bottleneck
* 2.3: Attribute-based Learning, Decision Trees, and Quinlan's ID3
* 2.4: GOLEM as an example of Relational Learning
* 2.5: Bratko's summary of the successes of Machine Learning in the
Turing Tradition, 1992
* 2.6: GOLEM's Discovery of a Law of Nature
* Chapter 3: How Advances in Machine Learning affect the Inductivist
Controversy
* 3.1: Bacon's Example of Heat
* 3.2: The Importance of Falsification
* 3.3: Bacon's Method has only recently come to be used
* 3.4: The Need for Background Knowledge
* Chapter 4: Logic and Programming and a New Framework for Logic
* 4.1: The Development of PROLOG
* 4.2: PROLOG as a Non-Monotonic Logic
* 4.3: Two Examples of Translations from One Logical System to Another
* 4.4: Logic = Inference + Control
* 4.5: PROLOG introduces Control into Deductive Logic
* 4.6: PROLOG and Certainty. Is Logic a priori or empirical?
* Chapter 5: Can there be an Inductive Logic?
* 5.1: The Divergence between Deductive and Inductive Logic (up to the
early 1970s)
* 5.2: Inductive Logic as Inference + Control
* 5.3: Confirmation Values as Control in a Deductive Logic
* 5.4: The Empirical Testing of Rival Logics
* Chapter 6: Do Gödel's Incompleteness Theorems place a Limit on
Artificial Intelligence?
* 6.1: Anxieties caused by Advances in AI
* 6.2: Informal Exposition of Gödel's Incompleteness Theorems
* 6.3: The Lucas Argument
* 6.4: Objections to the Lucas Argument: i) Possible Limitations on
Self-Knowledge
* 6.5: Objections to the Lucas Argument: ii) Possible Additions of
Learning Systems
* 6.6: Why Advances in Computing are more likely to Stimulate Human
Thinking than to Render it Superfluous
* Notes, References, Index