flex Expert System Toolkit - LPIS

More documents

Recommendations

Info

1. Introduction 10 Forward Chaining Forward chaining production rules in flex follow the classical if-then rule format. Forward chaining is data-driven and is very suitable for problems which involve too many possible outcomes to check by backward chaining, or where the final outcome is not known. The forward chaining inference engine cycles through the current rule agenda looking for rules whose if conditions can be satisfied, and selects a rule to use or fire by executing its then part. This typically side affects data values, which means that a different set of rules now have their conditions satisfiable. Flex extends the classical production rule with an optional explanation facility and dynamic scoring mechanism for resolving conflicts during rule selection. Rules can have multiple conclusions or actions (either positive or negative) in their then part . The rule selection and agenda update algorithms of the forward chaining engine are flexible, with many built-in algorithms and the option of applying user-defined algorithms. Backward Chaining Search Backward chaining rules, which correspond closely to Prolog predicates, are called relations in flex. They have a single conclusion that is true, if all the conditions can be proven. Backward chaining is often referred to as goaldriven, and is closely linked to the notion of provability. Search is one of the key characteristics of expert systems. There are normally many ways of combining or chaining rules together with data to infer new conclusions. How to examine only the relevant part of this search space is a serious consideration with regard to efficiency. The ordering of rules, the provision of meta-rules (rules about which rules to use) and conflict-resolution schemes are all ways of helping us produce a sensible search tree which we can investigate. Prolog-based systems tend to use a depth-first strategy, whereby a certain path is fully explored by checking related paths, combined with backtracking to go back and explore other possibilities when a dead-end is reached. Frames and Inheritance Frame hierarchies are similar to object-oriented hierarchies. They allow data to be stored in an abstract manner within a nested hierarchy with common properties automatically inherited through the hierarchy. This avoids the unnecessary duplication of information, simplifies code and provides a more readable and maintainable system. flex toolkit
1. Introduction 11 Each frame or instance has a set of slots that contain attributes describing the frame's characteristics. These slots are analogous to fields within records (using database terminology) except that their expressive power is greatly extended. Frames inherit attribute-values from other frames according to their position in the frame hierarchy. This inheritance of characteristics is automatic, but can be controlled using different built-in algorithms. Questions and Answers Flex has a built-in question and answer sub-system that allows final applications to query the user for additional input via interactive dialogs. These screens can be simple pre-defined ones, or complex, sophisticated screens constructed using Prolog's own screen handling facilities and then attached to the question and answer sub-system. Explanations Flex has a built-in explanation system which supports both how and why explanations. Explanations can be attached to both rules and questions using simple because clauses. Data-driven Programming Flex offers special procedures which can be attached to collections of frames, individual frames or slots within frames. These procedures remain dormant until activated by the accessing or updating of the particular structure to which they have been attached. There are four different types of data-driven procedures available within flex: launches, demons, watchdogs, and constraints. Knowledge Specification Language Flex has its own expressive English-like Knowledge Specification Language (KSL) for defining rules, frames and procedures. The KSL enables developers to write simple and concise statements about the expert's world and produce virtually self-documenting knowledge-bases which can be understood and maintained by non-programmers. The KSL supports mathematical, boolean and conditional expressions and functions along with set abstractions; furthermore, the KSL is extendable through synonyms and templates. By supporting both logical and global variables in rules, flex avoids unnecessary rule duplication and requires fewer rules than most other expert systems. 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 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 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-
Page 46 and 47: 4. Data-Driven Programming 44 Const
Page 48 and 49: 4. Data-Driven Programming 46 • c
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
Page 62 and 63:
6. Anatomy of a flex Program 60 and
Page 64 and 65:
7. Knowledge Specification Language
Page 66 and 67:
Page 68 and 69:
Page 70 and 71:
Page 72 and 73:
Page 74 and 75:
Page 76 and 77:
Page 78 and 79:
Page 80 and 81:
Page 82 and 83:
Page 84 and 85:
Page 86 and 87:
Page 88 and 89:
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
Page 102 and 103:
Page 104 and 105:
Page 106 and 107:
Page 108 and 109:
Page 110 and 111:
8. Run-Time Interpretation of KSL 1
Page 112 and 113:
Page 114 and 115:
Page 116 and 117:
Page 118 and 119:
Page 120 and 121:
Page 122 and 123:
9. Flex Predicates 120 9. flex Tool
Page 124 and 125:
9. Flex Predicates 122 ask( +Name )
Page 126 and 127:
9. Flex Predicates 124 flex toolkit
Page 128 and 129:
9. Flex Predicates 126 every_instan
Page 130 and 131:
9. Flex Predicates 128 forward_chai
Page 132 and 133:
9. Flex Predicates 130 Examples fle
Page 134 and 135:
9. Flex Predicates 132 are : Search
Page 136 and 137:
9. Flex Predicates 134 isa_demon( ?
Page 138 and 139:
9. Flex Predicates 136 Example ?- i
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
9. Flex Predicates 142 new_group( +
Page 146 and 147:
9. Flex Predicates 144 one of the f
Page 148 and 149:
9. Flex Predicates 146 new_synonym(
Page 150 and 151:
9. Flex Predicates 148 the agenda.
Page 152 and 153:
9. Flex Predicates 150 Examples rem
Page 154 and 155:
9. Flex Predicates 152 remove_quest
Page 156 and 157:
9. Flex Predicates 154 spied_chain
Page 158 and 159:
9. Flex Predicates 156 If the exist
Page 160 and 161:
11. Example - Robbie Goes Shopping
Page 162 and 163:
Page 164 and 165:
Page 166 and 167:
Page 168 and 169:
Appendix A - Examples 166 Appendix
Page 170 and 171:
tail Appendix A - Examples 168 herb
Page 172 and 173:
Appendix A - Examples 170 Represent
Page 174 and 175:
Appendix A - Examples 172 The Ident
Page 176 and 177:
Appendix A - Examples 174 Example 2
Page 178 and 179:
Appendix A - Examples 176 The Rules
Page 180 and 181:
Appendix A - Examples 178 group sta
Page 182 and 183:
Appendix B - Formal Definition of K
Page 184 and 185:
Page 186 and 187:
Page 188 and 189:
Page 190 and 191:
Page 192 and 193:
Page 194 and 195:
Page 196 and 197:
Page 198 and 199:
Page 200 and 201:
Page 202 and 203:
Page 204 and 205:
Appendix C - KSL Keyword Glossary 2
Page 206 and 207:
Page 208 and 209:
Page 210 and 211:
Page 212 and 213:
Page 214 and 215:
Page 216 and 217:
Page 218 and 219:
Page 220 and 221:
Page 222 and 223:
Page 224 and 225:
Page 226 and 227:
Page 228 and 229:
Appendix D - Dealing with Uncertain
Page 230 and 231:
Page 232 and 233:
Page 234 and 235:
Index 232 — GENERAL INDEX — —
Page 236 and 237:
Index 234 cycle, 111 cycle/3, 106 c
Page 238 and 239:
Index 236 attribute of, 51 child, 7
Page 240 and 241:
Index 238 isa_exception/1, 117 isa_
Page 242 and 243:
Index 240 Ordering, 58, 59, 88, 105
Page 244 and 245:
Index 242 create, 128 disabling of,
Page 246 and 247:
Index 244 Updating agenda, 29, 74 U
Page 248 and 249:
Index 246 false, 180, 193 first, 72
Page 250 and 251:
Index 248 —flex PREDICATES INDEX
Page 252 and 253:
Index 250 run_data/0, 137 run_data/
show all

flex Expert System Toolkit - LPIS

Create successful ePaper yourself

Delete template?

Save as template?