Rigid Flexibility

Preface Acknowledgment PART I. Theoretical Foundation Chapter 1. The Goal of Artificial Intelligence 1.1 To define intelligence 1.2 Various schools in AI research 1.3 AI as a whole Chapter 2. A New Approach Toward AI 2.1 To define AI 2.2 Intelligent reasoning systems 2.3 Major design issues of NARS PART II. Non-Axiomatic Reasoning System Chapter 3. The Core Logic 3.1 NAL-0: binary inheritance 3.2 The language of NAL-1 3.3 The inference rules of NAL-1 Chapter 4. First-Order Inference 4.1 Compound terms 4.2 NAL-2: sets and variants of inheritance 4.3 NAL-3: intersections and differences 4.4 NAL-4: products, images, and ordinary relations Chapter 5. Higher-Order Inference 5.1 NAL-5: statements as terms 5.2 NAL-6: statements with variables 5.3 NAL-7: temporal statements 5.4 NAL-8: procedural statements Chapter 6. Inference Control 6.1 Task management 6.2 Memory structure 6.3 Inference processes 6.4 Budget assessment . PART III. Comparison and Discussion Chapter 7. Semantics 7.1 Experience vs. model 7.2 Extension and intension 7.3 Meaning of term 7.4 Truth of statement Chapter 8. Uncertainty 8.1 The non-numerical approaches 8.2 The fuzzy approach 8.3 The Bayesian approach 8.4 Other probabilistic approaches 8.5 Unified representation of uncertainty Chapter 9. Inference Rules 9.1 Deduction 9.2 Induction 9.3 Abduction 9.4 Implication Chapter 10. NAL as a Logic 10.1 NAL as a term logic 10.2 NAL vs. predicate logic 10.3 Logic and AI 11.1 Concept and categorization 11.2 Learning in NARS Chapter 12. Control and Computation 12.1 NARS and theoretical computer science 12.2 Various assumptions about resources 12.3 Dynamic natures of NARS PART IV. Conclusions Chapter 13. Current Results 13.1 Theoretical foundation 13.2 Formal model 13.3 Computer implementation Chapter 14. NARS in the Future 14.1 Next steps of the project 14.2 What NARS is not 14.3 General implications Bibliography Index