Artificial Intelligence Laboratory
Subject Overview
The practical companion to Artificial Intelligence, implementing search algorithms (DFS, BFS, Hill Climbing, A*), propositional logic evaluation, NLP preprocessing with NLTK, and mini projects such as Minimax game trees, N-Queens CSP, rule-based chatbots and CNN-based image classification. A 1-credit core lab paper.
Unit-wise Syllabus
1 units — click WhatsApp below to get the full notes for each
Unit 1: AI algorithm implementation and mini projects
Depth-First Search on a graph, Water Jug problem via BFS, Hill Climbing search, A* Search on a grid, propositional logic expression evaluation, optimization for maximum value in a list, NLP tasks with NLTK (tokenizing, stop-word filtering, stemming, POS tagging, chunking, NER); mini projects — Minimax for 2-player games, 4-Queens CSP, Magic Square constraint propagation, rule-based expert system, simple decision-making AI agent, rule-based chatbot, CNN image classification