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