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E-grāmata: Non-axiomatic Logic: A Model Of Intelligent Reasoning

(Temple Univ, Usa)
  • Formāts: 276 pages
  • Izdošanas datums: 03-May-2013
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • Valoda: eng
  • ISBN-13: 9789814440295
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  • Bibliotēkām
  • Formāts: 276 pages
  • Izdošanas datums: 03-May-2013
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • Valoda: eng
  • ISBN-13: 9789814440295
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This book provides a systematic and comprehensive description of Non-Axiomatic Logic, which is the result of the author's research for about three decades.Non-Axiomatic Logic is designed to provide a uniform logical foundation for Artificial Intelligence, as well as an abstract description of the laws of thought followed by the human mind. Different from mathematical logic, where the focus is the regularity required when demonstrating mathematical conclusions, Non-Axiomatic Logic is an attempt to return to the original aim of logic, that is, to formulate the regularity in actual human thinking. To achieve this goal, the logic is designed under the assumption that the system has insufficient knowledge and resources with respect to the problems to be solved, so that the logical conclusions are only valid with respect to the available knowledge and resources. Reasoning processes according to this logic covers cognitive functions like learning, planning, decision making, problem solving, etc.This book is written for researchers and students in Artificial Intelligence and Cognitive Science, and can be used as a textbook for courses at graduate level, or upper-level undergraduate, on Non-Axiomatic Logic.
Preface xi
Acknowledgments xiii
List of Tables
xv
1 Introduction
1(12)
1.1 Intelligence
1(3)
1.2 Reasoning System
4(5)
1.3 NAL Overview
9(4)
2 IL-1: Idealized Situation
13(12)
2.1 Categorical Language
14(2)
2.2 Experience-Grounded Semantics
16(5)
2.3 Syllogistic Inference Rules
21(4)
3 NAL-1: Basic Syntax and Semantics
25(16)
3.1 Evidence and its Measurement
25(3)
3.2 Two-Dimensional Truth-Value
28(3)
3.3 Representations of Uncertainty
31(4)
3.4 Experience and Belief
35(6)
4 NAL-1: Basic Inference Rules
41(24)
4.1 Local Inference Rules
41(8)
4.2 Forward Inference Rules
49(13)
4.3 Backward Inference Rules
62(3)
5 NARS: Basic Memory and Control
65(12)
5.1 Inference Tasks
65(2)
5.2 Bag-Based Storage
67(2)
5.3 Concept as a Unit
69(2)
5.4 Inference Cycle
71(3)
5.5 Properties of NARS
74(3)
6 NAL-2: Derivative Copulas
77(12)
6.1 Similarity Copula
77(6)
6.2 Instance Copula
83(2)
6.3 Property Copula
85(4)
7 NAL-3: Set-Theoretic Terms
89(18)
7.1 Compound Term
89(3)
7.2 Intersections
92(4)
7.3 Differences
96(3)
7.4 Multi-Component Sets
99(2)
7.5 Inference on Compound Terms
101(6)
8 NAL-4: Relational Terms
107(10)
8.1 Product and Acquired Relation
107(3)
8.2 Types of Conceptual Relation
110(2)
8.3 Image and Structural Transformation
112(5)
9 NAL-5: Statements as Terms
117(20)
9.1 Higher-Order Statement
117(2)
9.2 Implication and Inheritance
119(5)
9.3 Implication as Conditional
124(5)
9.4 Negation
129(3)
9.5 Analytic Truth in Inference
132(5)
10 NAL-6: Variable Terms
137(18)
10.1 Variable Terms Defined
137(6)
10.2 Variable Elimination and Introduction
143(5)
10.3 Symbolic Reasoning
148(7)
11 NAL-7: Events as Statements
155(14)
11.1 Time and Events
155(4)
11.2 Temporal Connectors and Copulas
159(4)
11.3 Temporal Inference
163(6)
12 NAL-8: Operations and Goals as Events
169(16)
12.1 Operations as Executable Events
169(4)
12.2 Goals as Desired Events
173(2)
12.3 Practical Reasoning
175(5)
12.4 Sensorimotor Interface
180(5)
13 NAL-9: Self-Monitoring and Self-Control
185(12)
13.1 Mental Operations
185(5)
13.2 Feeling and Emotion
190(3)
13.3 Consciousness
193(4)
14 Summary and Beyond
197(20)
14.1 The Nature of NAL
197(4)
14.2 Comparison with other Logics
201(8)
14.3 NAL and AI
209(8)
Appendix A Narsese Grammar 217(4)
Appendix B NAL Inference Rules 221(8)
Appendix C NAL Truth-Value Functions 229(2)
Appendix D Proofs of Theorems 231(10)
Bibliography 241(10)
Index 251