UNIQUE SERIES AKTU Syllabus
BCS751BCS751H

Artificial Intelligence LAB

L-T-P0-0-2 Credits1 Session2025-26 TypeLab / Practical
Appears in 4 semesters · 4 branches

B.Tech Computer Science & Engineering · Sem 7 (Lab, code BCS751) · B.Tech Information Technology · Sem 7 (Lab, code BCS751) · B.Tech Computer Science & Design · Sem 7 (Lab, code BCS751) · B.Tech Computer Science (Hindi) · Sem 7 (Lab, code BCS751H)

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List of Experiments / Activities

  1. ARTIFICIAL INTELLIGENCE LAB
  2. 1Implement Breadth First Search (BFS) for a given graph or maze.
  3. 2Implement Depth First Search (DFS) for a tree or graph structure.
  4. 3Solve the 8-Puzzle Problem using A* Search Algorithm.
  5. 4Implement Hill Climbing Algorithm for numerical optimization or pathfinding.
  6. 5Implement Simulated Annealing Algorithm for constraint-based search problems.
  7. 6Solve Water Jug Problem using state-space search (BFS or DFS).
  8. 7Write Prolog programs to define family relationships using predicates.
  9. 8Implement 4-Queens Problem in Prolog using backtracking.
  10. 9Implement Unification Algorithm in Python or Prolog.
  11. 10Implement Forward and Backward Chaining in a rule-based system (manual or code-based).
  12. 11Demonstrate Resolution in Propositional Logic through a basic example (e.g., proving a theorem).
  13. 12Remove punctuation and stop words from a paragraph using nltk.
  14. 13Perform stemming and lemmatization on user-input text.
  15. 14Apply POS (Part of Speech) tagging using NLTK on a given sentence.
  16. 15Build a simple text classifier using NLTK (e.g., classify messages as spam/ham).
  17. 16Implement Tic-Tac-Toe game with a basic AI opponent.
  18. 17Implement Min-Max (Minimax) Algorithm for decision making in turn-based games.
  19. 18Enhance the game with Alpha-Beta Pruning to optimize Min-Max.
  20. 19Simulate a Vacuum Cleaner Agent that intelligently cleans a 2D environment.
  21. 20Build a simple chatbot using rules or pre-trained logic (can use regex or basic intent matching).
  22. 21Design a Constraint Satisfaction Problem solver, e.g., Sudoku, or Map Coloring.
  23. 22Perform simple Bayesian reasoning for a probability-based decision problem (e.g., medical diagnosis).
  24. Instructions to the Instructor:
  25. 1The instructor may add/delete/modify/tune experiments depending on syllabus coverage and availability of tools.
  26. 2All experiments should preferably be implemented using Python (with NLTK and standard libraries) and SWI-Prolog for logic programming tasks.
Course Outcomes (COs)5 outcomes

After completion of the course student will be able to:

  1. CO1Understand and apply basic search algorithms and intelligent problem-solving approaches in Python.K2
  2. CO2Design and implement logic-based knowledge representations and reasoning using Python and Prolog.K3
  3. CO3Use Natural Language Toolkit (NLTK) for basic NLP tasks like stemming, tagging, and classification.K3
  4. CO4Analyze and implement basic AI game strategies like Minimax and Alpha-Beta pruning.K4
  5. CO5Demonstrate AI techniques in real-world tasks such as text processing, planning, or constraint satisfaction.K4

Note: The IT syllabus PDF (CE&IT, CSIT, IT 2025-26) prints these course outcomes differently — CO1: level K2, K3; CO2: level K3, K4; CO3: level K3, K4; CO4: level K4, K5; CO5: level K4, K5.

Note: The AKTU syllabus PDF of some branches prints this subject with small differences in the course outcomes (CS & Design, CS (Hindi)). Please also check the official AKTU PDF of your branch, linked at the end of your semester page.

Official AKTU syllabus

This subject is on page 8 of the original AKTU PDF.

Official AKTU syllabus PDF ↗