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Artificial Intelligence and Scientific Methodby Donald Gillies
Synopses & Reviews
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, Gödel, 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.
Includes bibliographical references (p. -171) and 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.
Table of Contents
Chapter 1: The Inductivist Controversy, or Bacon vs. 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 70's)
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 G/D"odel's Incompleteness Theorems Place a Limit on Artificial Intelligence?
6.1. Anxietites Caused by Advances in AI
6.2. Informal Exposition of G/D"odel's Imcompleteness 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
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