Unit 1: Fundamentals of AI, Definition, History, Motivation, Goals, Contribution, Production System

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This Artificial Intelligence video lecture series will cover the following syllabus:

Syllabus: Artificial Intelligence

Unit 1: Fundamentals of Artificial Intelligence 1.1 Definition, history, motivation, and need for Artificial Intelligence (AI). 1.2 Goals and contributions of AI to modern technology. 1.3 Production systems: 1.3.1 Characteristics of production systems. 1.4 Search space and techniques: 1.4.1 Hill Climbing, Best First Search, Heuristic Search Algorithm. 1.4.2 A* and AO* Search Techniques.

Unit 2: Knowledge Representation 2.1 Problems in representing knowledge. 2.2 Knowledge representation methods: 2.2.1 Propositional Logic and Predicate Logic. 2.2.2 Comparison of Propositional and Predicate Logic. 2.3 Resolution and refutation. 2.4 Deduction, theorem proving, and inferencing. 2.5 Monotonic and non-monotonic reasoning.

Unit 3: Probabilistic Reasoning and Knowledge Structures 3.1 Probabilistic reasoning and Bayes' Theorem. 3.2 Semantic networks, scripts, schemas, and frames. 3.3 Conceptual dependency representation. 3.4 Forward and backward reasoning.

Unit 4: Game Playing and Natural Language Processing (NLP) 4.1 Game-playing techniques:
4.1.1 Minimax procedure. 4.1.2 Alpha-beta cut-offs. 4.2 Planning: 4.2.1 Study of the block world problem in robotics. 4.3 Introduction to understanding and Natural Language Processing (NLP): 4.3.1 Components of NLP. 4.3.2 Applications of NLP in designing expert systems.

Unit 5: Expert Systems 5.1 Definition and characteristics of Expert Systems (ES). 5.2 Requirements, components, and capabilities of Expert Systems. 5.3 Inference Engine: 5.3.1 Forward and backward chaining. 5.4 Limitations of Expert Systems. 5.5 Expert System Development Environment and technology. 5.6 Benefits of Expert Systems.

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