Artificial Intelligence and Scientific Method

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