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Swayam

Fundamentals of AI

NITTTR via Swayam

Overview

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To introduce students to the fundamental concepts and techniques of Artificial Intelligence (AI), enabling them to understand intelligent agents, search strategies, problem-solving techniques, planning, logic, and inference mechanisms for building AI-based solutions. Course Outcomes (COs) CO1: Explain the fundamental concepts, history, and scope of Artificial Intelligence. CO2: Apply heuristic and randomized search strategies to solve AI-related problems. CO3: Implement optimal path-finding and problem decomposition techniques in AI systems. CO4: Design planning strategies and apply constraint satisfaction techniques for AI problem-solving. CO5: Utilize propositional and first-order logic for inference and reasoning in AI applications.

Syllabus

Week 1: Introduction to Artificial Intelligence – History, Turing Test, Symbolic AI, and Agents

Week 2: Heuristic Search Techniques – Best First, Hill Climbing, Beam, and Tabu Search

Week 3: Randomized Search – Simulated Annealing, Genetic Algorithms, and Ant Colony Optimization

Week 4: Optimal Path-Finding – Branch & Bound, A*, IDA*, Beam Stack Search, Divide and Conquer

Week 5: Problem Decomposition – Goal Trees, AO*, Rule-Based Systems, and Rete Net

Week 6: Game Playing Strategies – Minimax, Alpha-Beta Pruning, and SSS* Algorithm

Week 7: Planning and Constraint Satisfaction – Goal Stack, Plan Space Planning, Graphplan, Constraint Propagation

Week 8: Logic and Inferences – Propositional Logic, First-Order Logic, Forward & Backward Chaining

Taught by

Dr.P.Selvi Rajendran

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