EVALUATING EXPLANATIONS:  A CONTENT THEORY

David B. Leake, Indiana University

Lawrence Erlbaum Associates, 1992.

ISBN 0-8058-1064-1 
Ordering information below

Overview:

Psychology and philosophy have long studied the nature and role of
explanation.  More recently, artificial intelligence research has
developed promising theories of how explanation facilitates learning
and generalization.  By using explanations to guide learning,
explanation-based methods allow reliable learning in complex
situations.

This volume addresses fundamental issues in generating and judging
explanations:  When to explain, what constitutes an explanation, how to
build explanations, and how to evaluate candidate explanations.  It
examines the problem of everyday explanation of anomalous events, and
argues that context---involving explainer goals, beliefs, and
experience---is crucial to generating and judging those explanations.
The theory developed is not only a theory of the _process_ of
explanation, but also of the _content_ of the knowledge required
to detect anomalies and guide search for explanations.

The book presents models of pattern-based anomaly detection as a means
to automatically generate appropriate target concepts for explanation;
of how the search for explanations of anomalies can be focused by
case-based reasoning; and of goal-based evaluation of candidate
explanations.  It describes the implementation of these theories in
ACCEPTER, a computer system that understands stories, detects
anomalous events, retrieves relevant explanations from memory, and
evaluates candidate explanations in light of overarching goals.


Contents:

ACKNOWLEDGMENTS   ix
OVERVIEW AND READER'S GUIDE xi

1.  EXPLANATION AND UNDERSTANDING   1
    1.1   Fashioning Beliefs   1
    1.2   Maintaining Beliefs During Understanding  4 
    1.3   Routine Understanding   5 
    1.4   Detecting Anomalies   6 
    1.5   Learning by Explaining Anomalies   10 
    1.6   What Is a Good Explanation?   12 
    1.7   Constructing Explanations   15 
    1.8   Implementing the Theory   16 
    1.9   Significance for Explanation-Based Systems   27 
    1.10  The Following Chapters   29 
2. PERSPECTIVE ON THE THEORY   31 
    2.1   The SWALE Project   32 
    2.2   ACCEPTER Overview and Sample Run   36 
    2.3   Comparison to Views from AI   53 
    2.4   Comparison to Views from Psychology   66 
    2.5   Comparison to Views from Philosophy   67 
    2.6   Summary   69 
3. ANOMALIES AND ROUTINE UNDERSTANDING   71 
    3.1   The Nature of Anomalies   71 
    3.2   ACCEPTER's Routine Understanding   72 
    3.3   Overview of ACCEPTER's Anomaly Detection   84 
    3.4   Conflicts with Specific Prior Expectations   84 
    3.5   The Need for Additional Tests   90 
4. PATTERN-BASED ANOMALY DETECTION   91 
    4.1   Overview of ACCEPTER's Pattern Types   92 
    4.2   ACCEPTER's Patterns and their Structure   94 
    4.3   Pattern Retrieval   106 
    4.4   The Need to Identify Underlying Problems   107 
    4.5   Finer Grained Checks   109 
    4.6   What ACCEPTER's Checks Miss   118 
    4.7   Judging ACCEPTER's Anomaly Detection   121 
    4.8   Summary   122 
5. ANOMALY CHARACTERIZATION   125 
    5.1   From Detection to Characterization   125 
    5.2   The Information Characterizations Include   126 
    5.3   How Characterization Guides Search   128 
    5.4   Mapping Conflicts to Characterizations   132 
    5.5   Defining the Content of Anomaly Categories   135 
6. A VOCABULARY FOR ANOMALIES   137 
    6.1   Overview of the Categories   138 
    6.2   SURPRISING-PLAN-CHOICE   143 
    6.3   SURPRISING-PROP-CHOICE   150 
    6.4   Conclusion   152 
7. NONMOTIVATIONAL ANOMALY TYPES   155 
    7.1   Overview of Nonmotivational Anomalies   155 
    7.2   PLAN-EXECUTION-FAILURE   156 
    7.3   BLOCKAGE-VIOLATION   162 
    7.4   PROCESS-EXECUTION-FAILURE  164 
    7.5   DEVICE-FAILURE   166 
    7.6   INFORMATION-FAILURE   167 
    7.7   UNUSUAL-FEATURE   168 
    7.8   FEATURE-DURATION-FAILURE   169 
    7.9   Judging the Vocabulary   170 
    7.10  Conclusion   171 
8. EVALUATING RELEVANCE AND PLAUSIBILITY   175 
    8.1   Accounting for Surprising Features   176 
    8.2   Accounting for Why Expectations Failed   182 
    8.3   Evaluating Plausibility   187 
    8.4   Conclusion   195 
9. FOCUSING ON IMPORTANT FACTORS   197 
    9.1   Each Anomaly Has Many Explanations   197 
    9.2   How Goals Affect Explanation   198 
    9.3   Major Explanation Purposes   201 
    9.4   How Explanation Purposes Arise   202 
    9.5   How Goals Affect ACCEPTER's Evaluation   205 
    9.6   Explaining to Predict   206 
    9.7   Explaining for Repair   216 
    9.8   Explaining for Control   220 
    9.9   Explaining to Assign Praise or Blame   226 
    9.10  Sketch of Requirements for Other Goals   228 
    9.11  Summary of Evaluation Dimensions   230 
    9.12  Goal-Based Adaptation   232 
    9.13  Conclusion   232 
10. CONCLUSIONS AND FUTURE DIRECTIONS   235 
    10.1  Summary and Significance of the Theory   235 
    10.2  Future Directions   237 
    10.3  Final Notes   241 
REFERENCES   243 
AUTHOR INDEX   255 
SUBJECT INDEX   257 


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