Course Details

Credits: 3

Hours per Week: L:3 T:0 P:0

Course Objectives

This course aims to:

• Introduce the fundamental concepts and techniques of Artificial Intelligence (AI).
• Provide an understanding of various AI problem-solving approaches, including search algorithms, knowledge representation, and reasoning.
• Explore applications of AI in different domains.
• Discuss the ethical and societal implications of AI.

Course Outcomes

After undergoing this course, the students will be able to:

1. Build intelligent agents for search and games
2. Solve AI problems by learning various algorithms and strategies
3. Understand probability as a tool to handle uncertainity
4. Learning optimization and inference algorithms for model learning
5. Design and develop programs for an reinforcement agent to learn and act in a structured environment

Detailed Syllabus

UNIT 1: Introduction

Concept of AI, history, current status, scope, agents, environments, Problem Formulations, Review of tree and graph structures, State space representation, Search graph and Search tree.

[8hrs] (CO 1)

UNIT 2: Search Algorithms

Random search, Search with closed and open list, Depth first and Breadth first search, Heuristic search, Best first search, A* algorithm, Game Search.

[9hrs] (CO 2)

UNIT 3: Probabilistic Reasoning

Probability, conditional probability, Bayes Rule, Bayesian Networks- representation, construction and inference, temporal model, hidden Markov model.

[6hrs] (CO 3)

UNIT 4: Markov Decision Process

MDP formulation, utility theory, utility functions, value iteration, policy iteration and partially observable MDPs.

[6hrs] (CO 4)

UNIT 5: Reinforcement Learning

Passive reinforcement learning, direct utility estimation, adaptive dynamic programming, temporal difference learning, active reinforcement learning- Q learning.

[6hrs] (CO 5)

Suggested Books

1. Stuart Russell and Peter Norvig, "Artificial Intelligence: A Modern Approach", 3rd Edition, Prentice Hall
2. Elaine Rich and Kevin Knight, "Artificial Intelligence", Tata McGraw Hill
3. Trivedi, M.C., "A Classical Approach to Artifical Intelligence", Khanna Publishing House, Delhi
4. Saroj Kaushik, "Artificial Intelligence", Cengage Learning India
5. David Poole and Alan Mackworth, "Artificial Intelligence: Foundations for Computational Agents", Cambridge University Press 2010

Course Details

Credits: 1

Hours per Week: L:0 T:0 P:2

List of Experiments

1. Write a programme to conduct uninformed and informed search.
2. Write a programme to conduct game search.
3. Write a programme to construct a Bayesian network from given data.
4. Write a programme to infer from the Bayesian network.
5. Write a programme to run value and policy iteration in a grid world.
6. Write a programme to do reinforcement learning in a grid world.

Course Outcomes

Upon completion of this lab, students will be able to:

1. Implement and experiment with various AI algorithms.
2. Analyze the performance of different AI techniques.
3. Apply AI techniques to solve real-world problems.
4. Gain hands-on experience with AI tools and libraries.