Introduction–What is intelligence? Foundations of artificial intelligence
(AI).History of AI. AI problems: Toy Problems, Real World problems- Tic-
Tac-Toe, Water Jug, Question-Answering, 8-puzzle, 8-Queens problem.
Formulating problems, Searching for Solutions.
Knowledge Representation: Propositional Logic, Propositional Theorem
proving-Inference and Proofs, Proof by Resolution, Horn Clauses and
definite Clauses, Forward and Backward chaining; First order Logic,
Inference in First order Logic.
Informed (Heuristic) Search Strategies-Hill Climbing, Simulated
Annealing, Greedy best-first search, A* and optimal search, Memorybounded
heuristic search.
Natural language processing: Grammars, Parsing, Semantic Analysis and
Pragmatics.
Introduction: What is Soft Computing? Difference between Hard and Soft
computing, Requirement of Soft computing, Major Areas of
Soft Computing, Applications of Soft Computing.
Neural Networks: Introduction, What is Neural Network, Learning rules
and various activation functions, Supervised Learning Networks, Single
layer Perceptrons, Back Propagation networks, Architecture of
Backpropagation(BP)Networks, Backpropagation Learning, Variation of
Standard Back propagation Neural Network, Introduction to Associative Memory, Adaptive Resonance theory and Self Organizing Map, Recent
Applications. Unsupervised Learning Networks.
Fuzzy Systems: Fuzzy Set theory, Fuzzy vs. Crisp set, Fuzzy Relation,
Fuzzification, Minmax Composition, Defuzzification Method, Fuzzy Logic,
Fuzzy Rule based systems, Predicate logic, Fuzzy Decision Making, Fuzzy
Control Systems, Fuzzy Classification.
Genetic Algorithm: History of Genetic Algorithms (GA), Working
Principle, Various Encoding methods, Fitness function, GA Operators-
Reproduction, Crossover, Mutation, Convergence of GA, Bit wise operation
in GA, Multi-level Optimization.
Introduction to Hybrid Systems