flex Expert System Toolkit - LPIS

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2. Frames and Inheritance 26 Universal Defaults Flex supports the notion of universal default values. These are default values which may be inherited by any frame in the hierarchy. For example, in a system representing physical objects the notion of weight is universal. It is the product of the object's volume and density. Rather than have a weight slot in each frame of the hierarchy, it would be more sensible to have a single global definition of weight which is universally accessible. This is accomplished by having a special frame called root, which is always considered when inheriting values. The inheritance algorithm can be directed to visit the root frame either before or after visiting any ancestor frames. Singular Versus Multiple Inheritance ? Whenever a frame system allows for values to be inherited, there is the possibility for alternative answers according to where the inheritance comes from. This is in many ways similar to Prolog itself, which allows for alternative answers to the same query. The plurality of inheritance within flex can either be singular (the default) or multiple. In both cases the search stops as soon as the first value is found. For singular inheritance there is a commitment to this first value, and no others will ever be considered. For multiple inheritance, however, alternative values can be obtained by backtracking using the inheritance algorithm, which is a Prolog program. In reality, the singular inheritance algorithm is the same as the multiple inheritance algorithm except that it terminates with a cut (!) to avoid any potential backtracking. Frame Relationships In its default setting, the only relationship between frames is the AKO (akind-of) hierarchy which defines how values are to be inherited. In general, though, it would be of great benefit to be able to define other relationships between frames, such as all tigers can hunt humans. flex toolkit
2. Frames and Inheritance 27 Example frame tiger . frame human . relation can_hunt( tiger, human ) . tiger can_hunt human In its present form, the extension of the can_hunt/2 relation contains only a single tuple, namely the pair . If we were to pose the Prolog query … ?- prove( can_hunt( X, Y ) ) . there would be a single solution which binds the indentifier tiger to the variable X, and binds the indentifier human to the variable Y. (prove/1 is a built-in flex predicate, described later.) Now consider two particular instances of tiger and human. Example instance shere_khan is a tiger . instance mowgli is a human . tiger can_hunt human shere_khan mowgli The answers to our query above will remain the same, namely only a single solution. This is because the underlying logic only allows unification between objects which have the same name (i.e. pattern-matching). The query ?- prove( can_hunt( shere_khan, mowgli ) ) . would fail because shere_khan does not match tiger, and furthermore because mowgli does not match human. flex toolkit
Page 1: flex Reference By Dave Westwood
Page 4 and 5: The contents of this manual describ
Page 6 and 7: Contents 4 Contents Contents 4 1. I
Page 8 and 9: Contents 6 KSL Sentences 81 Frames
Page 10 and 11: Contents 8 Variant 196 Set 197 Gene
Page 12 and 13: 1. Introduction 10 Forward Chaining
Page 14 and 15: 2. Frames and Inheritance 12 2. Fra
Page 16 and 17: 2. Frames and Inheritance 14 Defaul
Page 18 and 19: 2. Frames and Inheritance 16 flex t
Page 20 and 21: 2. Frames and Inheritance 18 define
Page 22 and 23: 2. Frames and Inheritance 20 employ
Page 24 and 25: 2. Frames and Inheritance 22 Inheri
Page 26 and 27: 2. Frames and Inheritance 24 Depth-
Page 30 and 31: 2. Frames and Inheritance 28 The fl
Page 32 and 33: 3. Forward Chaining 30 the axioms a
Page 34 and 35: 3. Forward Chaining 32 flex toolkit
Page 36 and 37: 3. Forward Chaining 34 A Simple Mod
Page 38 and 39: 3. Forward Chaining 36 The basic cy
Page 40 and 41: 3. Forward Chaining 38 First Come F
Page 42 and 43: 3. Forward Chaining 40 considered.
Page 44 and 45: 4. Data-Driven Programming 42 data-
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Page 50 and 51: 4. Data-Driven Programming 48 conte
Page 52 and 53: 5. Questions And Answers 50 Rather
Page 54 and 55: 5. Questions And Answers 52 Customi
Page 56 and 57: 5. Questions And Answers 54 answer
Page 58 and 59: 6. Anatomy of a flex Program 56 ?-
Page 60 and 61: 6. Anatomy of a flex Program 58 fle
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8. Run-Time Interpretation of KSL 1
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9. Flex Predicates 120 9. flex Tool
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9. Flex Predicates 122 ask( +Name )
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9. Flex Predicates 124 flex toolkit
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9. Flex Predicates 126 every_instan
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9. Flex Predicates 128 forward_chai
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9. Flex Predicates 130 Examples fle
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9. Flex Predicates 132 are : Search
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9. Flex Predicates 134 isa_demon( ?
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9. Flex Predicates 136 Example ?- i
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9. Flex Predicates 142 new_group( +
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9. Flex Predicates 144 one of the f
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9. Flex Predicates 146 new_synonym(
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9. Flex Predicates 148 the agenda.
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9. Flex Predicates 150 Examples rem
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9. Flex Predicates 152 remove_quest
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9. Flex Predicates 154 spied_chain
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9. Flex Predicates 156 If the exist
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11. Example - Robbie Goes Shopping
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Appendix A - Examples 166 Appendix
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tail Appendix A - Examples 168 herb
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Appendix A - Examples 170 Represent
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Appendix A - Examples 172 The Ident
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Appendix A - Examples 174 Example 2
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Appendix A - Examples 176 The Rules
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Appendix A - Examples 178 group sta
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Appendix B - Formal Definition of K
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Appendix C - KSL Keyword Glossary 2
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Appendix D - Dealing with Uncertain
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Index 232 — GENERAL INDEX — —
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Index 234 cycle, 111 cycle/3, 106 c
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Index 236 attribute of, 51 child, 7
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Index 238 isa_exception/1, 117 isa_
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Index 240 Ordering, 58, 59, 88, 105
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Index 242 create, 128 disabling of,
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Index 244 Updating agenda, 29, 74 U
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Index 246 false, 180, 193 first, 72
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Index 248 —flex PREDICATES INDEX
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Index 250 run_data/0, 137 run_data/
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