Artificial Intelligence Syllabus IOE

Evaluation Scheme:

The questions will cover all the chapters of the syllabus. The evaluation scheme is outlined in the table below:

*Note: There may be minor deviations in the marks distribution.

Course Content:

1. Introduction (4 hours)

1.1 Definition of Artificial Intelligence
1.2 Importance of Artificial Intelligence
1.3 AI and related fields
1.4 Brief history of Artificial Intelligence
1.5 Applications of Artificial Intelligence
1.6 Definition and importance of Knowledge and Learning

2. Problem Solving (4 hours)

2.1 Defining problems as a state-space search
2.2 Problem formulation
2.3 Problem types: Well-defined problems, Constraint satisfaction problems
2.4 Game playing, Production systems

3. Search Techniques (5 hours)

3.1 Uninformed search techniques: Depth-first search, Breadth-first search, Depth limit search, Search strategy comparison
3.2 Informed search techniques: Hill climbing, Best-first search, Greedy search, A* search
3.3 Adversarial search techniques: Minimax procedure, Alpha-beta pruning

4. Knowledge Representation, Inference, and Reasoning (8 hours)

4.1 Formal logic: Connectives, Truth tables, Syntax, Semantics, Tautology, Validity, Well-formed formulas
4.2 Propositional logic, Predicate logic, First-order Predicate Logic (FOPL), Interpretation, Quantification, Horn clauses
4.3 Rules of inference, Unification, Resolution refutation system (RRS), Answer extraction from RRS, Rule-based deduction system
4.4 Statistical reasoning: Probability and Bayes’ theorem, Causal networks, Reasoning in belief networks

5. Structured Knowledge Representation (4 hours)

5.1 Representations and Mappings
5.2 Approaches to Knowledge Representation
5.3 Issues in Knowledge Representation
5.4 Semantic networks, Frames
5.5 Conceptual dependencies and Scripts

6. Machine Learning (6 hours)

6.1 Concepts of learning
6.2 Learning by analogy, Inductive learning, Explanation-based learning
6.3 Neural networks
6.4 Genetic algorithms
6.5 Fuzzy learning
6.6 Boltzmann machines

7. Applications of AI (14 hours)

7.1 Neural networks
    7.1.1 Network structure
    7.1.2 Adaline network
    7.1.3 Perceptron
    7.1.4 Multilayer Perceptron, Backpropagation
    7.1.5 Hopfield network
    7.1.6 Kohonen network
7.2 Expert systems
    7.2.1 Architecture of an expert system
    7.2.2 Knowledge acquisition, Induction
    7.2.3 Knowledge representation: Declarative knowledge, Procedural knowledge
    7.2.4 Development of expert systems
7.3 Natural Language Processing and Machine Vision
    7.3.1 Levels of analysis: Phonetic, Syntactic, Semantic, Pragmatic
    7.3.2 Introduction to Machine Vision

Practical:

Laboratory exercises should be conducted using either LISP or PROLOG. Practical exercises must cover fundamental search techniques, simple question answering, inference, and reasoning.

References:

1. E. Rich and Knight, Artificial Intelligence, McGraw Hill, 2009.
2. D. W. Patterson, Artificial Intelligence and Expert Systems, Prentice Hall, 2010.
3. P. H. Winston, Artificial Intelligence, Addison Wesley, 2008.
4. Stuart Russel and Peter Norvig, Artificial Intelligence: A Modern Approach, Pearson, 2010.