I
1. The Pre-History of Artificial Intelligence
2. Computing, Representations, and Definitions of Artificial Intelligence
II
3. The State Space, Finite State Machines, and Artificial Life
4. Searching the State Space
5. Heuristic Search
6. Heuristics: 2-Person Games and Theoretical Constraints
III
7. Introduction to the Propositional and Predicate Calculi
8. The Predicate Calculus and Unification
9. Resolution: Reasoning with the Propositional and Predicate Calculi
IV
10. The Production System Representation and Search Engine
11. Advanced Applications of Symbol-Based AI: Planning and Learning
12. Uncertain Reasoning: Symbol Based
V
13. Introduction to Association-Based Knowledge Representations
14. Association-Based Representations: Frames, Conceptual Graphs, WordNet, and FrameNet
VI
15. An Introduction to Neural Networks
16. The Delta Rule, Backpropagation, and Matrix Representations
17. Deep Learning: Introduction and Representations
18. Building Language Models and Transformers
19. Alternative Network Architectures: Prototypes and Classifiers
20. Alternative Network Architectures: Attractor Networks and Memories
VII
21. Counting, the Foundation for Probabilities
22. Bayes’ Theorem
23. Bayesian Belief Networks and Observable Markov Models
24. Hidden Markov and Alternative Probabilistic Models
VIII
25. Artificial Intelligence: User’s Ethical Issues
26. AI Ethical Issues: From a Social Perspective
27. AI: Philosophical Perspectives, Current Limitations, and Future Promise