Preface
Contents
Contributors
1 The Scientific Work of Seiki Akama
1.1 Introduction
1.2 Biographical Information
1.3 Scientific Work
1.4 Books
References
2 A Busy-Beaver-Like Function in Complexity Theory
2.1 Introduction
2.2 Required Notation and Concepts: Function F
2.3 Kreisel's Counterexample Function to [P=NP]
2.4 Main Steps in Our Argument
2.5 The Crucial Step
2.6 f Is a Busy-Beaver-Like Function
2.7 Proof of Kreisel's Conjecture for f
2.8 More Exoticisms
2.9 Envoi
References
3 On the Choice of Primitives in Tense Logic
3.1 Introduction
3.2 Basic Tense Logic
3.3 Alternative Axiomatization
References
4 Paraconsistent Annotated Logic and Chaos Theory: Introducing the Fundamental Equations
4.1 Introduction
4.1.1 Literature Review
4.1.2 Non-Classical Paraconsistent Logic (PL)
4.2 The Logistic Map Equation and the Foundations of PAL2v
4.2.1 The ParaChaos Equations
4.2.2 Paraconsistent/Chaos Theory Equilibrium Point of Reference
4.3 Results of an Application of the ParaChaos Equations
4.3.1 Computer Simulations Results
4.4 Conclusions
References
5 A Paraconsistent Artificial Neural Cell of Learning by Contradiction Extraction (PANCLCTX) with Application Examples
5.1 Introduction
5.2 Paraconsistent Logic (PL)
5.2.1 Paraconsistent Artificial Neural Cell
5.2.2 Paraconsistent Artificial Neural Cell of Learning
5.3 Paraconsistent Artificial Neural Cell of Learning by Contradiction Extraction
5.4 Application Examples in the Industry
5.4.1 Variable Estimator Configured with PANCLCTX
5.4.2 Average Extractor with PANCLCTX
5.4.3 Temperature Measurement with PANCLCTX
5.5 Conclusions
References
6 Probabilistic Autoepistemic Equilibrium Logic
6.1 Syntax and Semantics of PE
6.2 Probabilistic Autoepistemic Equilibrium Logic
6.3 Conclusions
References
7 Rough-Set-Base Data Analysis: Theoretical Basis and Applications
7.1 Introduction
7.2 Rough Sets
7.2.1 Decision Table and Lower and Upper Approximations
7.2.2 Relative Reduct
7.2.3 Discernibility Matrix
7.2.4 Decision Rule
7.3 Heuristic Algorithm for Attribute Reduction Using Reduced Decision Tables
7.3.1 Conclusion of Section 7.3
7.4 Evaluation of Relative Reducts Using Partitions
7.4.1 Roughness of Partition and Average of Coverage of Decision Rules
7.4.2 Example
7.4.3 Conclusion of Section 7.4
7.5 An Example of Applications—Rough-Set-Based DNA Data Analysis
7.5.1 Background
7.5.2 Methodology
7.5.3 Datasets
7.5.4 Results and Discussion
7.5.5 Conclusion of Section 7.5
7.6 Summary
References
8 Bilattice Tableau Calculi with Rough Set Semantics
8.1 Introduction
8.2 Rough Set and Decision Logic
8.3 Four-Valued Logic and Bilattice
8.3.1 Belnap's Four-Valued Logic
8.3.2 Rough Sets Semantics for Bilattice
8.4 Bilattice-Based Tableau Calculi
8.5 Soundness and Completeness
8.6 Conclusion
References
9 Optimizing the Data Loss Prevention Level Using Logic Paraconsistent Annotated Evidential Eτ
9.1 Introduction
9.1.1 General Context
9.1.2 General Data Protection Law of Brazil
9.1.3 Artificial Intelligence
9.1.4 Machine Learning
9.2 Bibliographic Review
9.2.1 DLP—Data Loss Prevention
9.2.2 Paraconsistent Annotated Evidential Logic Eτ
9.2.3 Artificial Intelligence Techniques
9.2.4 Data Protection
9.3 Minimization of Data Loss
9.3.1 DLP—Data Loss Prevention Using Paraconsistent Annotated Evidential Logic Eτ
9.4 Tests
9.4.1 Python Program and Mass Data Results
9.5 Conclusion
References
10 Evaluation of Behavioural Skills Simulating Hiring of Project Manager Applying Paraconsistent Annotated Evidential Logic Eτ
10.1 Introduction
10.2 Selection Process
10.2.1 Interview
10.2.2 The Project Manager Candidate
10.2.3 Simulation
10.3 Paraconsistent Annotated Evidential Logic Eτ
10.4 Method
10.4.1 The Hypothetical Scenario
10.4.2 Expert Groups
10.4.3 Logic E Application Evaluating Candidate
10.5 Result
10.6 Discussion
10.7 Conclusion
References
11 A Paraconsistent Decision-Making Method
11.1 Introduction
11.2 The Unitary Square of the Cartesian Plane (USCP)
11.3 Decision Rule
11.4 NOT, OR and AND Operators of Logic Eτ
11.5 The Decision Making Process: Paraconsistent Decision-Making Method (PDM)
11.5.1 The Stages of the PDM
11.5.2 Analysis of Results
11.6 Conclusions and Observations
References
12 Annotated Logics and Application—An Overview
12.1 Introduction
12.1.1 Paraconsistent Logic
12.1.2 Initial Indirect Applications of Paconsistent Logics
12.1.3 Inheritance Nets
12.1.4 Object Oriented Database
12.2 Some Subsequent Applications
12.2.1 Logic Programming
12.2.2 Paraconsistent Annotated Evidential Logic Eτ
12.2.3 Expert Systems
12.2.4 Automatic Prediction of Stress in Piglets (Sus Scrofa)
12.2.5 Model for Paraconsistent Quality Assessment of Software Developed in Salesforce
12.2.6 About the Turning Point of Cache Efficiency in Computer Networks with Logic Eτ
12.2.7 Robotics
12.3 Conclusion
References