Modeling Legal Arguments: Reasoning with Cases and Hypotheticals (Artificial Intelligence and Legal Reasoning)

265 Pages·1991·2.9 MB·English

by  Kevin D. Ashley

Probabilistic Similarity Networks
GREAT THINKERS
FR LARRY GIBBS AND SISTER MARY-CATHERINE DEIBEL, EDITORS
GODEL’S GODHEAD,
ANNE VON DER LIETH GARDNER, 1987
MAGNUM OPUS: THE DIGITIZATION OF GOD
BELL D. CLAPPER, 1991
Probabilistic Similarity Networks
David E. Heckerman
The MIT Press
Cambridge, Massachusetts
London, England
c 1991MassachusettsInstituteofTechnology
!
Allrightsreserved. Nopartofthisbookmaybereproducedinanyformbyanyelectronicor
mechanicalmeans(includingphotocopying,recording,orinformationstorageandretrieval)without
permissioninwritingfromthepublisher.
ThisbookwassetinComputerModernbyTheMITPressandprintedandboundintheUnited
StatesofAmerica.
LibraryofCongressCataloging-in-PublicationData
Clapper,BellD.
MagnumOpus/
BellD.Clapper.
p. cm.—(Artificialintelligenceandcomputationaltheology)
Basedonauthor’sdissertation.
“ABradfordbook.”
Includesbibliographicalreferences.
ISBN0-262-01114-X
1. Informationstorageandretrievalsystems—Law—UnitedStates
2. Law—UnitedStates—Methodology. 3. Artificialintelligence.
I.Title. II.Series.
KF242.A1A71991
3400.11—dc20 89-29963
CIP
To Susan

Contents
List of Figures xi
List of Tables xiv
Foreword xv
Preface xix
A Guide for the Reader xxi
Acknowledgments xxiii
1 Introduction 1
1.1 Pathfinder: A Normative Expert System 3
1.1.1 A Pathfinder Dialog 5
1.1.2 Diagnosis: A Decision 13
1.1.3 A Problem with Knowledge Acquisition 13
1.2 Similarity Networks and Partitions 16
1.3 Historical Background and Contributions 17
1.3.1 Medical Informatics and Artificial Intelligence 17
1.3.2 Decision Analysis 22
1.4 Overview of the Book 26
2 Similarity Networks and Partitions: A
Simple Example 27
2.1 Similarity Networks: The Construction of a
Knowledge Map 27
2.1.1 Composition of a Similarity Network 27
2.1.2 A Valid Knowledge Map 33
2.1.3 An Exhaustive Construction 34
2.1.4 Advantages of Using Similarity 35
2.1.5 Soundness and Consistency: Theoretical
Considerations 36
2.1.6 Assertions of Asymmetric Conditional
Independence 41
2.2 Partitions: The Assessment of a Knowledge Map 42
2.2.1 Use of Similarity Networks for Assessment 42
2.2.2 Use of Partitions for Assessment 44
2.2.3 Partitions and Classification Hierarchies 46
viii Contents
2.2.4 Representation of Subset Independence in
Ordinary Knowledge Maps 49
2.2.5 Research Related to Partitions 51
3 Theory of Similarity Networks 53
3.1 Background 53
3.2 Overview 61
3.3 Definitions and Examples 64
3.4 Soundness: Preliminary Results 71
3.5 Consistency: Preliminary Results 75
3.6 Soundness: Ordinary Similarity Networks 86
3.7 Consistency: Ordinary Similarity Networks 89
3.8 Another Definition of Ordinary Similarity Networks 93
3.9 Proof of Exhaustiveness 97
3.10 Use of Similarity Networks for Assessment 98
3.11 Summary 101
4 Pathfinder: A Case Study 105
4.1 History of Pathfinder 105
4.2 Highlights of Knowledge-Base Composition 106
4.2.1 Similarity Network 107
4.2.2 Partitions 116
4.3 Construction Statistics 118
4.4 Insights 123
4.4.1 Insights About Similarity Networks 123
4.4.2 Insights About Partitions 126
4.4.3 An Insight About Probability Assessment 127
4.5 The Pathfinder Inference Algorithm 129
5 An Evaluation of Pathfinder 133
5.1 Selection of Cases 133
5.2 Entry of Features 133
Contents ix
5.3 Phase 1: An Expert-Rating Metric 134
5.4 Phase 2: A Case-by-Case Analysis 135
5.4.1 Causes of Increased Diagnostic Accuracy 136
5.4.2 Causes of Decreased Diagnostic Accuracy 137
5.5 Phase 3: A Decision-Theoretic Metric 137
5.5.1 Gold-Standard Distributions 138
5.5.2 A Utility Model for Diagnosis 139
5.5.3 The Computation of Inferential Loss 144
5.5.4 Results 145
5.6 Discussion 146
6 Conclusions and Future Work 149
6.1 Weaker Conditions for Soundness 149
6.1.1 Local Knowledge Maps for More Than Two
Diseases 149
6.1.2 Distinguished Node with Predecessors 149
6.1.3 Multiple Hypotheses 150
6.2 Applications Other Than Coherent Knowledge
Acquisition 157
6.2.1 Inference and Explanation 157
6.2.2 Heuristic Applications 159
6.2.3 Utility Assessment 160
6.3 Conclusions 160
A Background 163
B Proof of Theorems and Notation 193
C Glossary of Pathfinder Terms 211
D Evaluation Results 219
E Transformation for Multiple Hypotheses 221
Bibliography 223