rmes |

Introduction,i

1 A short history,1

1.1 Why bother about history?,1

1.2 In the beginning was the logic of philosophers ,2

1.3 The logic of mathematicians arrived next,3

1.4 First attempts at automating reasoning,4

1.5 At long last, the first expert systems appear,5

1.5.1 Seminal achievements,6

1.5.2 Commercial successes,8

1.6 Artificial Intelligence crisis - the 1970s and 1980s,9

1.6.1 The crisis,9

1.6.2 The aftermath,10

1.6.3 Where are we heading?,11

1.6.4 Why there is not much talk about expert systems?,12

2 What are expert systems?,13

2.1 A definition,13

2.2 Properties of expert systems,16

2.3 Benefits of expert systems,19

2.3.1 Managing human resources,19

2.3.2 Increasing efficiency and enhancing quality,20

2.4 Obstacles,22

2.5 Expert system deficiencies,24

2.6 Expert system applications,26

3 Basics of rmes ,29

3.1 Knowledge bases and variables in rmes ,29

3.2 Handling negation in rmes,31

3.3 Classification of knowledge bases for rmes ,33

3.4 Methodological assumptions,35

4 Elementary exact chaining - fundamentals,37

4.1 A definition,37

4.2 Elementary exact rule base,39

4.2.1 Properties,39

4.2.2 Cause-effect and situation-action,42

4.2.3 Other steps beyond logic,42

4.2.4 Open world assumption,43

4.2.5 Nesting of rules,46

4.2.6 Facts,47

4.2.7 Negating conclusions,47

4.2.8 Inconsistencies in rule bases,48

4.2.9 Flat rules and rule flattening,51

4.2.10 Nesting of rules once more,52

4.2.11 Redundancies in rule bases,53

4.3 Elementary exact constraint base,54

4.3.1 Structure and purpose,54

4.3.2 Mutually exclusive conclusions,55

4.3.3 Joint inconsistencies in rule and constraint bases,57

4.3.4 Joint redundancies in rule- and constraint bases,58

4.4 Elementary exact model base,59

4.4.1 Basic models,60

4.4.2 Extended models,62

4.4.3 Linear models,63

4.4.4 Polynomial models ,64

4.4.5 Nesting of elementary models,65

4.4.6 Known arguments and model facts,67

4.4.7 Inconsistencies in model base,68

4.4.8 Joint inconsistencies in model- and rule bases,69

4.4.9 Arithmetical models with same results,69

4.4.10 What if more advanced models are needed?,71

4.5 Elementary exact advice base and advice text files,71

4.6 Comments in bases,72

4.7 Abstract files,73

4.8 Elementary exact graphics base and graphics files,73

4.9 Elementary exact sound base and sound files,74

4.10 Goals and techniques of elementary exact chaining,75

4.11 Elementary exact forward chaining,78

4.11.1 The essentials,78

4.11.2 An example of forward chaining,80

4.11.3 An example of forward chaining with constraints,82

4.12 Elementary exact backward chaining,84

4.12.1 The essentials,84

4.12.2 An example of backward chaining,85

4.12.3 An example of backward chaining with constraints,86

4.13 Monotonicity of elementary exact chaining,86

5 Elementary exact chaining - examples,89

5.1 Abstract knowledge base,89

5.2 Knowledge base for car buying,92

5.3 Knowledge bases for diagnosing weight,95

5.3.1 WEIGHT1 knowledge base,95

5.3.2 WEIGHT2 knowledge base,103

5.3.3 WEIGHT3 knowledge base,109

5.4 Credit validation knowledge base,118

5.4.1 Domain knowledge,118

5.4.2 Decision space analysis,120

5.4.3 Designing the model base,125

5.4.4 Designing the constraint base,127

5.4.5 Testing the knowledge base - forward chaining,127

5.4.6 Testing the knowledge base - backward chaining,130

5.4.7 Getting rid of askable conditions?,132

5.5 Managerial knowledge base,133

5.5.1 Domain knowledge and knowledge base,133

5.5.2 Testing the knowledge base - forward chaining,141

5.5.3 Testing the knowledge base - backward chaining,144

5.6 Information granules and knowledge bases,146

5.6.1 A definition,146

5.6.2 Why information granules are needed?,146

5.7 Expert systems and legal reasoning,147

5.7.1 Introduction,147

5.7.2 Expert systems for deterministic legal domains,149

A Real-World example - the Bill of Laws for Initial Capital,149

The business potential of deterministic legal expert systems,159

5.7.3 Expert systems for non-deterministic legal domains,160

Basic criticism,160

5.7.4 Where are the difficulties?,161

5.7.5 What has been done?,163

5.7.6 Legal expert systems and democracy,163

5.7.7 Perspectives,164

6 Augmented exact chaining - fundamentals,165

6.1 Introduction,165

6.2 Definition,166

6.3 Augmented exact rule base,168

6.3.1 Properties,168

6.3.2 Closed world assumption,169

6.3.3 Nesting of rules,171

6.3.4 Flat rules and rule flattening,173

6.3.5 Inconsistencies in rule bases,174

External inconsistencies,175

Internal inconsistencies,177

Catch-22 - a famous set of IAE1-inconsistent rules ,179

6.3.6 Redundancies in rule bases,180

6.4 Augmented exact constraint base,182

6.4.1 Structure and purpose,182

6.4.2 Mutually exclusive conclusions,184

6.4.3 Joint inconsistencies in rule and constraint bases,186

6.4.4 Joint redundancies in rule- and constraint bases,187

6.5 Augmented Exact Model Base,188

6.5.1 Properties,188

6.5.2 Nesting,189

6.6 Augmented exact advice base and advice files ,191

6.7 Augmented exact graphics base and graphics files ,192

6.8 Augmented exact sound base and sound files,194

6.9 Goals and techniques of augmented exact chaining,195

6.10 Augmented exact forward chaining,197

6.10.1 The essentials,197

6.10.2 An example of forward chaining,201

6.11 Augmented exact backward chaining,203

6.11.1 The essentials,203

6.11.2 An example of backward chaining,205

7 Augmented exact chaining - examples,209

7.1 Abstract knowledge base,209

7.2 Knowledge base for car buying,212

7.3 Knowledge bases for diagnosing weight,214

7.3.1 WEIGHT1 knowledge base,214

7.3.2 Knowledge base WEIGHT2,222

7.3.3 Knowledge Base WEIGHT3,228

7.4 Credit validation knowledge base,237

7.4.1 Augmented base advantages,237

7.4.2 Knowledge base CRE,237

7.4.3 Testing the knowledge base - forward chaining,241

7.4.4 Testing the knowledge base - backward chaining,247

7.5 Managerial knowledge base,250

7.5.1 Advantages of augmented exact base,250

7.5.2 Testing the knowledge base - forward chaining,254

7.5.3 Testing the knowledge base - backward chaining,261

8 Modeling uncertainty,265

8.1 Beyond truth and falsehood: tertium datur!,266

8.2 Deficiencies of exact knowledge bases,267

8.3 Why take uncertainty into account?,269

8.3.1 A basic limitation,269

8.3.2 Some other limitations,270

8.3.3 Uncertainty and decision maker preferences,271

8.3.4 Another criticism of Aristotelian logic,271

8.3.5 Uncertainty has many faces,272

8.4 Probabilistic models,273

8.4.1 Meaning and interpretations,274

8.4.2 Why do we need probability?,275

8.4.3 Basic concepts,276

8.4.4 Making an assessment,279

8.5 Fuzzy models,280

8.5.1 Basic concepts,280

8.5.2 Crisp sets and fuzzy sets,281

8.5.3 Linguistic variables,283

8.5.4 Fuzzy models and probabilistic models,284

8.5.5 Operations on fuzzy sets,286

8.5.6 Fuzzy rules and fuzzy inference,288

8.5.7 Fuzzification and defuzzification,294

8.5.8 An example of fuzzy inference,296

8.5.9 Successes and problems,298

8.6 Modeling with certainty factors,299

8.7 Modified Certainty Factor Algebra,300

8.7.1 Main assumptions,300

8.7.2 Rules with same conclusion,305

8.7.3 Certainty factors for lists of conditions,307

8.7.4 Certainty factor for the conclusion of a rule,308

8.7.5 Certainty factor for cumulative rules - principles,311

8.7.6 Certainty factor for cumulative rules - examples,314

8.7.7 Certainty factor for disjunctive rules - principles,321

8.7.8 Certainty factor for disjunctive rules - examples,323

8.7.9 Certainty factors for negated conclusions of disjunctive rules - principles ,328

8.7.10 Certainty factors for negated conclusions of disjunctive rules - examples ,329

8.7.11 Non-standard usage of disjunctive rules,333

8.8 Where are certainty factors coming from?,334

8.8.1 Data mining,334

8.8.2 Preferences of decision makers,335

8.8.3 Certainty factors for askable conditions,336

8.8.4 Certainty factors for conclusions of relational models,337

8.8.5 Values of certainty factors,338

8.8.6 Sensitivity of conclusion certainty factors,339

9 Elementary uncertain chaining - fundamentals,341

9.1 A definition,341

9.2 Elementary uncertain rule base,342

9.2.1 What remains unchanged?,342

9.2.2 How to make a rule uncertain?,343

9.2.3 Nesting of rules, negating conclusions, askable and unaskable conditions ,344

9.2.4 Facts in elementary uncertain rule bases,346

9.2.5 Flattening of elementary uncertain rule bases,347

9.2.6 Inconsistencies in elementary uncertain rule bases,348

9.2.7 Redundancies in elementary uncertain rule bases,349

9.3 Elementary exact constraint base,351

9.3.1 Structure and purpose,351

9.3.2 Joint inconsistencies in uncertain rule bases and exact constraint bases,352

9.3.3 Joint redundancies in uncertain rule bases and exact constraint bases,353

9.4 Elementary uncertain constraint base,354

9.4.1 Joint inconsistencies in uncertain rule bases and uncertain constraint bases ,356

9.4.2 Joint redundancies in uncertain rule bases and uncertain constraint bases ,357

9.5 Elementary uncertain model base,358

9.6 Elementary uncertain advice base and advice text files ,361

9.7 Elementary uncertain graphics base and graphics files ,362

9.8 Elementary uncertain sound base and sound files,363

9.9 Goals and techniques of elementary uncertain chaining ,363

9.10 Elementary uncertain forward chaining,364

9.10.1 The essentials,364

9.10.2 An example of forward chaining,367

9.11 Elementary uncertain backward chaining,371

9.11.1 The essentials,371

9.11.2 An example of backward chaining,372

9.12 Monotonicity of uncertain chaining,373

10 Elementary uncertain chaining - examples,375

10.1 Uncertain chaining paradox,375

10.2 Abstract knowledge base,376

10.3 Knowledge bases for diagnosing weight,380

10.3.1 WEIGHT1 knowledge base,380

10.3.2 WEIGHT2 knowledge base,389

10.3.3 WEIGHT3 knowledge base,398

10.4 Credit validation knowledge base,411

10.5 Managerial knowledge base,431

10.6 Legal expert systems revisited,453

11 Augmented uncertain chaining - fundamentals,455

11.1 A definition,455

11.2 Elementary and augmented chaining,457

11.2.1 Negated conditions and conclusions,457

11.2.2 External inconsistencies,458

11.2.3 Internal inconsistencies,460

11.3 Augmented uncertain model bases,462

11.3.1 What remains unchanged?,462

11.3.2 Negatively uncertainly resolved relational models,462

11.4 Augmented uncertain advice base and advice files ,463

11.5 Augmented uncertain graphics base and graphics files ,465

11.6 Augmented uncertain sound base and sound files ,465

11.7 An example of forward chaining,465

11.8 An example of backward chaining,467

12 Augmented uncertain chaining - examples,473

12.1 Abstract knowledge base,473

12.2 Knowledge bases for diagnosing weight,478

12.2.1 WEIGHT1 knowledge base,478

12.2.2 WEIGHT2 knowledge base,487

12.2.3 WEIGHT3 knowledge base,496

12.3 Credit validation knowledge base,508

12.4 Managerial knowledge base,525

13 Conclusions and summary,547

Appendix 1. The Business Rules Manifesto,549

Glossary,552

References,563

Index,568

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