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Artificial Intelligence and Scientific Method


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Table of Contents

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 Goedel's Incompleteness Theorems place a Limit on Artificial Intelligence? 6.1: Anxieties caused by Advances in AI 6.2: Informal Exposition of Goedel'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

About the Author

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.


`crisp, clear and concise' THES `An old-fashioned monograph: tightly argued, heavily referenced.' New Scientist `This is an original and very interesting book ... it is obviously a good place to start for anyone who would like to examine the notions of logic and scientific method in the light of recent developments in artificial intelligence.' Peter Ohrstrom, Aalborg University `if you are not a philosopher this book is worth reading - but for interest alone ... If you know any philosophers, however, you should make sure they read it.' Mike James, Scientific Computing World, June 1997 'offers an interseting view on recent developments in AI, particularly in machine learning, form a philosopher's perspective. the book is of value to all AI practioneers' Zentralblatt Math

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