A Paraconsistent Decision-Making Method

Foreword......Page 3
Preface......Page 5
Contents......Page 9
1.1 Preliminary Concepts......Page 13
1.2 Classical Logic......Page 15
1.3 The Non-classical Logics......Page 17
1.4 Paraconsistent Logic......Page 19
1.5 Paraconsistent Annotated Logic (PAL)......Page 21
1.6 Lattice Associated to the Paraconsistent Annotated Logic......Page 22
1.7 Axiomatization of the Paraconsistent Annotated Logic Qτ......Page 24
2.1 General Aspects......Page 29
2.2 Lattice of the Annotation Constants......Page 31
2.4 Connectives of Conjunction, Disjunction and Implication......Page 34
2.5 Lattice τ......Page 35
2.6 The Lattice τ and the Decision States......Page 38
2.7 Logical Eτ Operators (NOT, MAX and MIN)......Page 42
3.1 General Considerations......Page 48
3.2 Requirement Level and the Decision Rule......Page 50
4.1 Initial Considerations......Page 52
4.2.2 Choice of the Influence Factors......Page 54
4.2.3 Establishment of the Sections for Each Factor......Page 55
4.2.4 Construction of the Database......Page 56
4.2.5 Field Research......Page 58
4.2.6 Calculation of the Resulting Annotations......Page 59
4.2.7 Determination of the Center of Gravity......Page 66
4.2.8 Decision Making......Page 67
5.1 Search for the Expert’s Opinions on the Database, Once the Research Result Is Known (Stage 5)......Page 68
5.2 Obtention of the Resulting Values from the Favorable Evidence and the Contrary Evidence for Each One of the Factors (Stage 6)......Page 70
5.3 Calculation of the Values of the Degrees of Evidence, Favorable and Contrary, of the Center of Gravity (Stage 7)......Page 72
5.4 The Decision Making (Stage 8)......Page 73
5.5 The Construction of the Para-Analyzer Algorithm (Chart)......Page 76
6 Application Examples......Page 82
6.1.1 Establishment of the Requirement Level......Page 84
6.1.2 Selection of the Influence Factors and Establishment of the Sections......Page 85
6.1.4 Field Research......Page 87
6.1.5 Obtention of the Resultant Degrees of Favorable and Contrary Evidences for the Factors......Page 89
6.1.6 Obtention of the Degrees of Favorable and Contrary Evidences of the Center of Gravity......Page 90
6.1.7 Analysis of the Results......Page 92
6.2 Feasibility Analysis of the Launch of a Product......Page 95
6.2.2 Selection of the Influence Factors and Establishment of the Sections......Page 96
6.2.3 Construction of the Database......Page 98
6.2.4 Field Research and Calculation of the Resultant Degrees of Favorable Evidence and Contrary Evidence for the Factors and Center of Gravity......Page 100
6.2.5 Analysis of the Results......Page 102
6.3.1 Establishment of the Requirement Level......Page 112
6.3.2 Selection of Factors and Establishment of the Sections......Page 113
6.3.3 Construction of the Database......Page 114
6.3.4 Field Research and Obtention of the Results......Page 115
6.3.5 Analysis of the Results and Final Decision......Page 117
6.3.7 Influence of the Requirement Level......Page 119
6.4 Feasibility Analysis of the Implementation of a Manufacturing System that Utilizes Advanced Technologies......Page 124
6.4.1 Performance Coefficient of a New Manufacturing System Compared to the Old One, for a Certain Influence Factor......Page 125
6.4.3 Identifying the Influence Factors (Attributes or Indicators)......Page 126
6.4.5 Construction of the Database......Page 128
6.4.6 Feasibility Analysis of the Implementation of a Flexible Manufacturing System......Page 134
6.4.7 Analysis of the Results......Page 135
6.5 Diagnosis Prediction......Page 138
6.5.1 Construction of the Database......Page 141
6.5.2 Calculation of the Resultant Degree of Certainty for Each Disease as a Result of the Symptoms Presented by the Patient......Page 144
6.5.3 The Obtention of the Foreseen Diagnosis......Page 149
6.5.4 Restriction to Accept the Foreseen Diagnosis......Page 150
7.1 An Example to Substantiate the Comparison......Page 160
7.2 A Short Review of the Statistical Decision Method (SDM)......Page 163
7.3 Comparison Between PDM and SDM: The Distribution of the Degree of Certainty (H)......Page 165
7.4 Comparison Between PDM and SDM: The Adherent Normal Curve (ANC)......Page 168
7.5 Comparison Between PDM and SDM: Comparing the Decisions......Page 170
7.6 Another View of the Application of the Statistic......Page 172
8.1.1 Theoretical Basis......Page 176
8.1.2 Application of the Fuzzy Logic in Decision Making......Page 180
8.1.3 A Simple Application of the SVFDM (Negoita [85] with Shimizu [94])......Page 181
8.1.4 Another Application of the SVFDM (Shimizu [94], p. 65)......Page 182
8.2.1 Solution by the Paraconsistent Decision Method—PDM......Page 184
8.2.2 Solution by the Simplified Version of the Fuzzy Method (SVFDM)......Page 189
8.3 Comparison Between the Two Methods......Page 192
9 Example from Everyday Life......Page 196
10.1 Introduction......Page 203
10.2 Automation and Robotics......Page 207
10.3 Paraconsistent Knowledge in Distributed Systems......Page 210
10.5 Paraconsistent Frame System......Page 211
10.8 Paraconsistent Artificial Neural Networks......Page 212
10.9 Conclusions......Page 213
Biblio......Page 214