Course Objective:
The objectives of the course are
- Understand fundamental concepts of Artificial Intelligence
- – Explore different AI search techniques and intelligent agent architecture
- – Study logical reasoning, knowledge representation, and inference
- – Gain exposure to game theory and expert system design
- – Implement basic AI algorithms and applications in Python
Course Outcomes:
After completion of the course students will be able to:
CO1. Understand the scope and history of Artificial Intelligence and its real-world applications
CO2. Formulate AI problems and solve them using appropriate search strategies
CO3. Analyze and design intelligent agents and their behavior
CO4. Apply logical reasoning for knowledge representation and inference
CO5. Develop simple expert systems and AI-based applications
Module 1: Introduction to AI (4 hours)
– Definition and goals of AI
– History and evolution
– Types of AI: Narrow, General, Super
– Applications in various domains
– Ethical considerations in AI
Module 2: Intelligent Agents (4 hours)
– Agents and environments
– PEAS (Performance, Environment, Actuators, Sensors)
– Agent types: simple reflex, model-based
– Structure of intelligent agents
– Rationality and autonomy
Module 3: Uninformed Search Techniques (8 hours)
– Problem formulation
– Breadth-first search (BFS)
– Depth-first search (DFS)
– Uniform cost search
– Depth-limited and iterative deepening search
Module 4: Informed Search and Heuristics (8 hours)
– Best-first search
– A* algorithm
– Heuristic functions and their properties
– Hill climbing
– Simulated annealing and local search
Module 5: Game Playing (6 hours)
– Adversarial search concepts
– Minimax algorithm
– Alpha-beta pruning
– Evaluation functions
– Game trees and cutoffs
Module 6: Knowledge Representation (8 hours)
– Propositional logic
– First-order predicate logic
– Unification and resolution
– Inference mechanisms
– Forward and backward chaining
Module 7: Expert Systems and Applications (6 hours)
– Rule-based systems
– Architecture of expert systems
– Case studies: MYCIN, DENDRAL
– Agent architectures
– Chatbots and AI applications
Practicals:
– Implement BFS and DFS in Python for maze problems
– Apply A* search on heuristic grid path planning
– Simulate tic-tac-toe using minimax with alpha-beta pruning
– Develop a rule-based medical diagnosis system
– Model a grid-world intelligent agent using PEAS representation
– Create a chatbot with hardcoded logical responses
– Build a simple decision-making system using predicate logic
Recommended Textbooks:
1.Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach”, 4th Edition, Pearson
2.Elaine Rich, Kevin Knight, and Shivashankar B. Nair, “Artificial Intelligence”, McGraw-Hill
3.Ivan Bratko, “Prolog Programming for Artificial Intelligence”, Pearson