Artificial Intelligence & Soft Computing
Subject Overview
The first Elective-II option, covering classical AI — problem formulation, knowledge representation via propositional and first-order logic, heuristic search strategies and natural language processing — and soft computing — neural networks, fuzzy systems and genetic algorithms. A 4-credit elective theory paper.
Unit-wise Syllabus
4 units — click WhatsApp below to get the full notes for each
Unit 1: AI foundations and knowledge representation
Foundations and history of AI, toy and real-world problems (Tic-Tac-Toe, Water Jug, 8-puzzle, 8-Queens), problem formulation and search, propositional logic and theorem proving, resolution, Horn clauses, forward/backward chaining, first-order logic and inference
Unit 2: Heuristic search and NLP
Hill climbing, simulated annealing, greedy best-first search, A* and optimal search, memory-bounded heuristic search, NLP grammars, parsing, semantic analysis and pragmatics
Unit 3: Neural networks
Soft computing vs hard computing, major areas and applications of soft computing, neural network learning rules and activation functions, single-layer perceptrons, backpropagation networks and architecture, associative memory, adaptive resonance theory, self-organizing maps, unsupervised learning networks
Unit 4: Fuzzy systems and genetic algorithms
Fuzzy set theory, fuzzy vs crisp sets, fuzzy relations, fuzzification, min-max composition, defuzzification, fuzzy logic and rule-based systems, fuzzy decision making and control, genetic algorithm history, encoding methods, fitness functions, GA operators (reproduction, crossover, mutation), convergence, introduction to hybrid systems