[comp.ai.philosophy] Information about new book on scientific discovery

morgan@unix.SRI.COM (Morgan Kaufmann) (11/16/90)

Morgan Kaufmann announces a new title in its Series on Machine
Learning, edited by Pat Langley (NASA Ames Research Center and
ISLE)

 
                    COMPUTATIONAL MODELS OF 
           SCIENTIFIC DISCOVERY AND THEORY FORMATION 
 
      Edited by Jeff Shrager (Xerox PARC) and Pat Langley 
 
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 the
evaluation of competing hypotheses.  However, 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.   
 

Available now.
498 pages, clothbound
Price: $39.95 (ordering information follows contents) 
ISBN 1-55860-131-7

------------------------------------------------------
                        Table of Contents

Chapter 1      Computational Approaches to Scientific Discovery
                    Jeff Shrager and Pat Langley

Chapter 2      The Conceptual Structure of the Geological
               Revolution
                    Paul Thagard and Greg Nowak

Chapter 3      On Finding the Most Probable Model
                    Peter Cheeseman

Chapter 4      An Integrated Approach to Empirical Discovery
                    Bernd Nordhaussen and Pat Langley

Chapter 5      Deriving Laws Through Analysis of Processes and
               Equations
                    Jan M. Zytkow

Chapter 6      A Unified Approach to Explanation and Theory
               Formation
                    Brian Falkenhainer

Chapter 7      Theory Formation by Abduction:  A Case Study Based
               on the Chemical Revolution
                    Paul O'Rorke, Steven Morris, and David
                    Schulenburg

Chapter 8      A Computational Approach to Theory Revision
                    Shankar Rajamoney

Chapter 9      Experimentation in Machine Discovery
                    Deepak Kulkarni and Herbert A. Simon

Chapter 10     Hypothesis Formation as Design
                    Peter D. Karp

Chapter 11     Diagnosing and Fixing Faults in Theories
                    Lindley Darden
               Appendix A    Dale Moberg and John Josephson

Chapter 12     Designing Good Experiments To Test Bad Hypotheses
                    David Klahr, Kevin Dunbar, and Anne L. Fay

Chapter 13     Scientific Discovery in the Layperson
                    Michael J. Pazzani and Margot Flowers

Chapter 14     Commonsense Perception and the Psychology of Theory
               Formation
                    Jeff Shrager

Chapter 15     Five Questions for Computationalists
                    Ryan D. Tweney

-------------------------------------------------------------


THE MORGAN KAUFMANN SERIES IN MACHINE LEARNING, EDITED BY PAT 
LANGLEY 
 
Produced in cooperation with the Institute for the Study of
Learning and Expertise, the series reports recent progress in the
study of mechanisms that learn from experience and improve their
performance over time. 
 
Another Title of Interest: 
 
READINGS IN MACHINE LEARNING, edited by Jude Shavlik (University of
Wisconsin-Madison) and Thomas Dietterich (Oregon State University) 

Available now
853 pages, softbound
ISBN 1-55860-143-0
Price: $39.95

_________________________________________________________________

Ordering Information:

     
These books are available now at better technical bookstores or by
ordering directly from the publisher at: 

     Morgan Kaufmann Publishers
     2929 Campus Drive, Suite 260
     San Mateo, CA 
     94403
     USA
     
     Phone: (800)745-READ, (415) 578-9911
     Fax: (415) 578-0672
     

For shipping, please add: 

     $3.50 for the first book and $2.50 for each additional for
     book rate shipping to the U.S. and Canada (2-3 weeks); 

     $6.50 for the first book and $3.50 for each additional for
     surface shipping to all other areas.  (4-8 weeks)

     Call or fax for quotations on other shipping methods.


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