Artificial Intelligence (CS4813)

Pre-requisite(s)

Computer Programming (CS-1123)

Data Structures (CS-2143)

Recommended Book(s)

George F. Luger: Artificial Intelligence - Structures And Strategies For Complex Problem Solving, 6th Edition, Addison-Wesley Publishing Company, 2008, ISBN 978-0321545893

Reference Book(s)

Stuart J. Russell And Peter Norvig: Artificial Intelligence – A Modern Approach, 3rd Edition, Prentice-Hall Publishing Co., 2009, ISBN 978-0136042594.

Course Objectives

By the end of this course, the students would be able to solve real-world problems using AI techniques. The students will also have good understanding of the various application areas of AI. They will become familiar with the current research in AI and the challenges currently being faced.

Course Learning Outcomes (CLO)

Course Objectives

Course Contents

Introduction

Introduction to the course
Defining AI
Turing’s Test
Chinese Room Experiment
Application Areas of AI

 Agents in AI

Agent Based Approach to AI
Types of Agents and their Environments
Introduction to Predicate Calculus
Inference Rules

 Concepts of Prolog

Introduction to Prolog
Facts and Rules
Search and Unification
Backtracking
Recursion Based Search in Prolog
Using fail and cut Predicates to Control the Search
Using Lists
Implementing ADTs in Prolog

 Searching Algorithms

Problem Solving by State Space Search
Formulating a Real World Problem as a State Space Search Problem
Depth-First and Breadth-First Search
Variations of Basic Search Algorithms
Informed Search
Heuristic Functions
Best-First Search Algorithms
A* Search

 Heuristic Functions

Admissible and Monotonic Heuristics
Informedness of a Heuristic Function

 Game Programming

Game Programming
Minimax Procedure
Alpha-Beta Pruning
Constraint Satisfaction Problems

 Expert Systems

Introduction to Expert Systems
Design of rule-based Expert Systems
Knowledge Engineering and Knowledge Representation
Expert System Shells
Techniques for Managing Uncertainty in Expert Systems

 Language Analysis

Natural Language Processing
Stages of Language Analysis
Chomsky’s Hierarchy of Grammars
Transition Network Parsers and Other Parsing Methods

 Concepts of Learning

Symbol Based Learning
Version Space Search
Candidate Elimination Algorithm
Unsupervised Learning
Neural Network Based Learning
Perceptron Learning
Backpropagation Learning
Competitive Learning

Mapping of CLOs to Assessment Modules

Final Exam

Assignments 

Surprise Tests/Quizzes

Project

Midterm Exam