Soft computing evaluation logic : the LSP decision method and its applications /: the LSP decision method and its applications. (2018)

• Record Type: Book
• Further Information: Note: Jozo Dujmovic.
• Authors: Dujmovic, Jozo, 1941-
• Contents: Preface xvii About the Companion Website xxiii Previous Publications xxiv Acknowledgments xxv List of Symbols and Abbreviations xxvii Part One EVALUATION DECISION PROBLEMS 1 1.1 Intuitive Evaluation as a Logic Decision Process 5 1.1.1 Main Observable Steps of the Intuitive Evaluation Process 6 1.1.2 Subjective and Objective Components in Evaluation 18 1.2 Quantitative Evaluation—An Introductory Example 21 1.2.1 Stakeholders and Their Goals 21 1.2.2 Attributes 22 1.2.3 Attribute Criteria 23 1.2.4 Simple Direct Ranking 27 1.2.5 Aggregation of Attribute Suitability Degrees 29 1.2.6 Using Cost and Suitability to Compute the Overall Value 32 1.3 Drawbacks of Simple Additive and Multiplicative Scoring and Utility Models 35 1.3.1 Simple Additive Scoring: The Irresistible Attractiveness of Simplicity 36 1.3.2 Simple Multiplicative Scoring 45 1.3.3 Logic Unsuitability of Scoring and Utility Theory Models in Professional Evaluation 47 1.4 Introduction to Professional Quantitative Evaluation 51 1.4.1 Five Fundamental Types of Professional Evaluation Problems 51 1.4.2 A Survey of Typical Professional Evaluation Problems 54 1.4.3 Components of Methodology for Professional Quantitative Evaluation 58 Part Two GRADED LOGIC AND AGGREGATION 63 2.1 Graded Logic as a Generalization of Classical Boolean Logic 69 2.1.1 Aggregators and Their Classification 70 2.1.1.1 Means 71 2.1.1.2 General Aggregation Functions 71 2.1.1.3 Logic Aggregators 73 2.1.1.4 Triangular Norms and Conorms 73 2.1.2 How Do Human Beings Aggregate Subjective Categories? 75 2.1.3 Definition and Classification of Logic Aggregators 85 2.1.4 Logic Bisection, Trisection, and Quadrisection of the Unit Hypercube 92 2.1.5 Propositions, Value Statements, Graded Logic, and Fuzzy Logic 95 2.1.6 Classical Bivalent Boolean Logic 100 2.1.7 Six Generalizations of Bivalent Boolean Logic 108 2.1.7.1 Expansion of Function Domain 109 2.1.7.2 Expansion of Logic Domain 111 2.1.7.3 Expansion of Annihilator Adjustability 112 2.1.7.4 Expansion of Semantic Domain 115 2.1.7.5 Expansion of Compensative Logic Functions 117 2.1.7.6 Expansion of the Range of Andness/Orness from Drastic Conjunction to Drastic Disjunction 118 2.1.8 GL Conjecture: Ten Necessary and Sufficient GL Functions 123 2.1.9 Basic Idempotent GL Aggregators 127 2.1.10 A Summary of Differences between Graded Logic and Bivalent Boolean Logic 134 2.1.11 Relationships between Graded Logic, Perceptual Computing, and Fuzzy Logic 136 2.1.12 A Brief History of Graded Logic 142 2.2 Observable Properties of Human Evaluation Logic 147 2.2.1 Perceptual Computer and Its Basic Properties 152 2.2.2 Simultaneity and Substitutability in Evaluation Models 177 2.2.3 Basic Semantic Aspects of Evaluation Logic Reasoning 190 2.2.4 Multipolarity: Grouping and Aggregation of Semantically Heterogeneous Inputs 212 2.2.5 Grouping and Aggregation of Semantically Homogeneous Inputs 218 2.2.6 Imprecision, Incompleteness, Logic Inconsistency, and Errors 222 2.3 Andness and Orness 237 2.3.1 A General Definition of Andness/Orness 237 2.3.2 Local Andness and Orness in the Simplest Case of Two Variables 239 2.3.3 Variability of Local Andness 242 2.3.4 Mean Local Andness and Orness in the Case of Two Variables 248 2.3.5 Local and Mean Local Andness and Orness in the Case of n Variables 251 2.3.6 Global Andness and Orness 253 2.3.7 Mean Global Andness/Orness Theorems and Their Applications 272 2.3.8 Geometric Interpretations of Andness and Orness 275 2.4 Graded Conjunction/Disjunction and Logic Modeling of Simultaneity and Substitutability 283 2.4.1 Definitions and Basic Mathematical Properties of Logic Aggregators 284 2.4.2 Classification of Conjunctive and Disjunctive Logic Aggregators 295 2.4.3 Properties of Means Used in Logic Aggregation 298 2.4.4 Algebraic Properties of Aggregators Based on Weighted Power Means 304 2.4.5 Logic Aggregators Based on Weighted Means with Adjustable Andness/Orness 313 2.4.6 Selection and Use of the Threshold Andness Aggregator 318 2.4.7 Andness-Directed Interpolative GCD Aggregators 327 2.4.8 Uniform and Nonuniform Interpolative GCD Aggregators 334 2.4.8.1 The Uniform Interpolative GCD Aggregator (UGCD) 334 2.4.8.2 An Extremely Soft Interpolative Aggregator 338 2.4.8.3 An Extremely Hard Interpolative Aggregator 338 2.4.9 Extending GCD to Include Hyperconjunction and Hyperdisjunction 342 2.4.10 From Drastic Conjunction to Drastic Disjunction: A General GCD Aggregator 347 2.4.11 Gamma Aggregators versus Extended GCD Aggregators 348 2.4.11.1 Multiplicative and Additive Gamma Aggregators 351 2.4.11.2 Comparison of Gamma Aggregators and GCD 355 2.4.12 Four Main Families of GCD Aggregators and Sixteen Conditions They Must Satisfy 361 2.5 The Percept of Importance and the Use of Weights 367 2.5.1 Multiplicative, Implicative, and Exponential Weights as Importance Quantifiers 369 2.5.1.1 Multiplicative Weights 370 2.5.1.2 Implicative Weights and the Weighted Conjunction/Disjunction 374 2.5.1.3 Exponential Weights 390 2.5.2 Impact of Weights on Aggregation Results 393 2.5.3 Semantic Components in Logic Aggregation Models 398 2.5.4 Seven Techniques for Weight Adjustment 402 2.5.4.1 Importance Decomposition Method 402 2.5.4.2 Direct Weight Assessment 405 2.5.4.3 Weights Based on Ranking 405 2.5.4.4 Weights Based on Menu 407 2.5.4.5 Collective Weight Determination 409 2.5.4.6 Weights Obtained from Pairwise Comparisons 411 2.5.4.7 Weights Based on Preferential Neuron Training 414 2.5.5 Multivariate Weighted Aggregation Based on Binary Aggregation Trees 417 2.6 Partial Absorption: A Fundamental Asymmetric Aggregator 429 2.6.1 Conjunctive Partial Absorption 430 2.6.2 Disjunctive Partial Absorption 436 2.6.3 Visualizing the Partial Absorption Function, Penalty, and Reward 439 2.6.4 Mathematical Models of Penalty and Reward 442 2.6.5 Selecting Parameters of Partial Absorption 449 2.7 Logic Functions That Use Negation 453 2.7.1 Negation and De Morgan’s Duality 453 2.7.2 De Morgan’s Laws for Weighted Aggregators and Dualized Weighted Aggregators 455 2.7.3 De Morgan’s Duals of Compound Functions 458 2.7.4 Nonidempotent Logic Functions 460 2.8 Penalty-Controlled Missingness-Tolerant Aggregation 463 2.8.1 Missing Data in Evaluation Problems 463 2.8.2 Penalty-Controlled Numerical Coding of Missing Data 465 2.8.3 A Penalty-Controlled Missingness-Tolerant Aggregation Algorithm 467 2.8.4 The Impact of Penalty on Missingness-Tolerant Aggregation 472 2.9 Rating Scales and Verbalization 475 2.9.1 Design of Rating Scales 476 2.9.1.1 Strict Monotonicity of Linguistic Labeling 477 2.9.1.2 Linearity of Rating Scales 483 2.9.1.3 Balance of Rating Scales 486 2.9.1.4 Cardinality of Rating Scales 488 2.9.1.5 Hybrid Rating Scales 489 2.9.2 Stepwise Refinement of Rating Scales for Andness and Orness 491 2.9.3 Scaling and Verbalizing Degrees of Importance 496 2.9.4 Scaling and Verbalizing Degrees of Suitability/Preference 497 Part Three LSP METHOD 499 3.1 An Overview of the LSP Method 501 3.1.1 Characterization of Stakeholder and Organization of an Evaluation Project 503 3.1.2 Development of the Suitability Attribute Tree 506 3.1.3 Elementary Attribute Criteria 514 3.1.4 Logic Aggregation of Suitability 519 3.1.4.1 Logic Aggregation Using Graded Conjunction/ Disjunction 523 3.1.4.2 Logic Aggregation Using Partial Absorption 526 3.1.5 Cost/Suitability Analysis and Comparison
• Edition: 1st
• Publisher Details: Hoboken : Wiley-IEEE Press
• Publication Date: 2018
• Extent: 1 online resource
• Subjects: 006.3; Soft computing; Evaluation -- Methodology; Artificial intelligence
• Languages: English
• ISBNs: 9781119256472; 9781119256465
• Related ISBNs: 9781119256458
• Notes: Note: Description based on CIP data; resource not viewed.
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