flex Expert System Toolkit - LPIS

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2. Frames and Inheritance 28 The flex system allows the underlying logic to be changed from one involving unification to one involving inheritance. That is, although … tiger does not match shere_khan human does not match mowgli with an inheritance logic we can show that … tiger is an ancestor of shere_khan in the frame hierarchy human is an ancestor of mowgli in the frame hierarchy and as such we can conclude that the tiger shere_khan can hunt the human mowgli. The underlying logic can be changed by issuing the Prolog command … ?- new_logic( inherit ) . It should be noted at this point that there is a general overhead involved in changing from a unification-based logic to an inheritance-based logic. That is, every procedure invocation will involve data lookup rather than direct pattern-matching. It should also be noted that the inheritance-based logic can only be used for checking given values, and not for generating instances of relationships. For example, it can check that shere_khan can hunt mowgli, but will not be able to generate it. flex toolkit
3. Forward Chaining 29 3. Forward Chaining and Rules In this chapter we shall describe the rules and inference engine of the forward chaining system and contrast it with backward chaining. Rules are really the life-blood of expert systems technology, and provide elegant, expressive and intuitive means of expressing knowledge. A simple rule may be, 'if it is raining then carry an umbrella'. Then given the fact that 'it is raining', we can infer or derive that we should 'carry an umbrella'. Facts can be thought of as degenerate cases of rules, i.e. rules without any pre-conditions, or rules whose pre-condition part is always true. Facts may be stored in a local database, retrieved from some external database, obtained through user question and answer interaction, stored as global variables, or derived from other rules. Another rule may be 'if it is raining then the ground is wet'. Now, given the same fact that 'it is raining', we can also infer or derive that 'the ground is wet', and store this as a fact, or, if ground is an attribute of some frame, set its value to wet. The collection of all facts is often referred to as the fact base. In rule-based programming we use an inference engine to match rules against facts to produce new facts which means we can then use new rules, until we reach some final state. Trying to prove the pre-conditions of forward chaining rules may well involve some backward chaining evaluation of sub-goals, i.e. there may be a backward chaining program for determining the current state of the weather. The integration of the two inference mechanisms can be as simple or as complicated as required. Notice, in the absence of information, execution fails and conditions in effect are deemed false. This is known as the closed world assumption. We shall use the following propositional example to show the difference between backward chaining inferences and forward chaining inferences. A if B and C . B if D . C . D . The first two statements are rules, and the second two statements are axioms (or facts). Forward Chaining Forward chaining inference is very much akin to the way in which mathematical proofs are presented (but not necessarily arrived at). Using 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 28 and 29: 2. Frames and Inheritance 26 Univer
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
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Page 42 and 43: 3. Forward Chaining 40 considered.
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Page 56 and 57: 5. Questions And Answers 54 answer
Page 58 and 59: 6. Anatomy of a flex Program 56 ?-
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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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flex Expert System Toolkit - LPIS

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