Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge

Overview

Complex machines are used, understood and repaired by people with essentially no formal training in physics and engineering-although the design and manufacture of such machines requires a deep knowledge of these subjects, and advanced mathematical reasoning. How is this possible? The way people understand these devices, the conceptual frameworks or "mental models" which they use, must be different from the differential equation based descriptions of the world taught in school; people's models are acquired ...

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Overview

Complex machines are used, understood and repaired by people with essentially no formal training in physics and engineering-although the design and manufacture of such machines requires a deep knowledge of these subjects, and advanced mathematical reasoning. How is this possible? The way people understand these devices, the conceptual frameworks or "mental models" which they use, must be different from the differential equation based descriptions of the world taught in school; people's models are acquired informally, are largely qualitative, use causal reasoning, and relate directly to the language we speak every day. They are crude,but successful.This collection of articles, which constituted a special issue of the Journal of Artificial Intelligence, presents the most recent work on qualitative reasoning about the real (physical) world. A common theme of all the contributions is explaining how physical systems work-from heat flow to transistors to digital computation. The explanations are so detailed and exact that they can be used by computer programs to reason about physical work in the same kinds of ways that people do.This rapidly developing area of cognitive science, variously called Qualitative or Naive Physics, is of direct psychological interest and has strong connections to theories of linguistic semantics. But there are also immediate technological applications, with much of this work funded by industry in the expectation of building computer systems which can communicate sensibly with people about physical mechanisms.Daniel G. Bobrow is a Research Fellow in the Intelligent Systems Laboratory, Xerox Palo Alto Research Center, editor-in-chief of the Journal of Artificial Intelligence, and Chair of the Governing Board of the Cognitive Science Society.This book inaugurates the series Computational Models of Cognition and Perception.A Bradford Book.

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Editorial Reviews

Booknews
Presents, within a conceptually unified theoretical framework, a body of methods that have been developed over the past 15 years for building and simulating qualitative models of physical systems--bathtubs, tea kettles, automobiles, the physiology of the body, chemical processing plants, control systems, electrical systems--where knowledge of the system is incomplete. The primary tool for this work is the author's QSIM algorithm, which is discussed in detail. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780262515405
  • Publisher: MIT Press
  • Publication date: 8/29/1994
  • Series: Artificial Intelligence Series
  • Pages: 450
  • Product dimensions: 7.20 (w) x 9.10 (h) x 1.30 (d)

Table of Contents

List of Figures
List of Tables
Series Foreword
Preface
1 Introduction to Qualitative Reasoning 1
2 Concepts of Qualitative Simulation 17
3 The QSIM Representation 37
4 Solving Qualitative Constraints 75
5 Dynamic Qualitative Simulation 97
6 Case Studies: Elementary Qualitative Models 131
7 Comparative Statics 151
8 Region Transitions 175
9 Semi-Quantitative Reasoning 203
10 Higher-Order Derivatives 237
11 Global Dynamical Constraints 269
12 Time-Scale Abstraction 299
13 Component-Connection Models 321
14 Compositional Modeling 351
A Glossary 381
B QSIM Functions 385
C Creating and Debugging a QSIM Model 391
References 397
Index 411
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