Computational models of scientific discovery and theory formation
著者
書誌事項
Computational models of scientific discovery and theory formation
（The Morgan Kaufmann series in machine learning）
Morgan Kaufmann Publishers, c1990
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注記
Includes index
内容説明・目次
内容説明
Scientific discovery has long fascinated both philosophers and historians, but only in the past few decades have the tools become available that enable computers to model this complex process. This collection reports on recent advances in the study of scientific discovery and theory formation based on the computational techniques of artificial intelligence and cognitive science. As the chapters in this book demonstrate, the last few years have seen the work on this topic expand dramatically from a few isolated efforts into a major enterprise, involving researchers from many disciplines and focusing on many aspects of scientific behavior. The contributors to this volume come from a variety of paradigms, including artificial intelligence, cognitive psychology, and the philosophy and history of science. The topics studied also range widely, including the discovery of empirical laws, the formation and revision of theories, the design of experiments, and and the evaluation of competing hypotheses. Despite this diversity, both researchers and approaches are united in their goal of developing and understanding computational mechanisms that demonstrate scientific behavior.
Many of the chapters focus on historical examples from the fields of physics, chemistry, geology, and biology, giving enlightening accounts of discovery in these domains. The chapters in this volume provide convincing evidence that the techniques of AI and cognitive science can produce coherent models of complex scientific behavior.
目次
1 Computational Approaches to Scientific Discovery, by Jeff Shrager and Pat Langley 2 The Conceptual Structure of the Geological Revolution, by Paul Thagard and Greg Nowak 3 On Finding the Most Probable Model, by Peter Cheeseman 4 An Integrated Approach to Empirical Discovery, by Bernd Nordhausen and Pat Langley 5 Deriving Laws Through Analysis of Processes and Equations, by Jan M. Zytkow 6. A Unified Approach to Explanation and Theory Formation, by Brian Falkenhainer 7 Theory Formation by Abduction: A Case Study Based on the Chemical Revolution, by Paul O'Rorke, Steven Morris, and David Schulenberg 8 A Computational Approach to Theory Revision, by Shankar Rajamoney 9 Experimentation in Machine Discovery, by Deepak Kulkarni and Herbert A. Simon 10. Hypothesis Formation As Design, by Peter D. Karp 11. Diagnosing and Fixing Faults in Theories, by Lindley Darden Appendix A Dale Moberg and John Josephson 12 Designing Good Experiments To Test Bad Hypotheses, by David Klahr, Kevin Dunbar, and Anne L. Fay 13. Scientific Discovery in the Layperson, by Michael J. Pazzani and Margot Flowers 14 Commonsense Perception and the Psychology of Theory Formation, by Jeff Shrager 15 Five Questions for Computationalists, by Ryan D. Tweney
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