Artificial Intelligence

Course Title: Artificial Intelligence

Course No: CSC266

Nature of the Course: Theory + Lab

Semester: 4

Full Marks: 60 + 20 + 20

Pass Marks: 24 + 8 + 8

Credit Hours: 3

Course Description

The course introduces the ideas and techniques underlying the principles and design of artificial intelligent systems. The course covers the basics and applications of AI, including: design of intelligent agents, problem solving, searching, knowledge representation systems, probabilistic reasoning, neural networks, machine learning and natural language processing.

Course Objectives

The main objective of the course is to introduce fundamental concepts of Artificial Intelligence. The general objectives are to learn about computer systems that exhibit intelligent behavior, design intelligent agents, identify AI problems and solve the problems, design knowledge representation and expert systems, design neural networks for solving problems, identify different machine learning paradigms and identify their practical applications.

Course Contents

1. Introduction

3 hrs

1.1. Artificial Intelligence (AI), AI Perspectives: acting and thinking humanly, acting and thinking rationally

1.2. History of AI

1.3. Foundations of AI

1.4. Applications of AI

2. Intelligent Agents

4 hrs

2.1. Introduction of agents, Structure of Intelligent agent, Properties of Intelligent Agents

2.2. Configuration of Agents, PEAS description of Agents

2.3. Types of Agents

Simple Reflexive

Model Based

Goal Based

Utility Based

2.4. Environment Types

Deterministic

Stochastic

Static

Dynamic

Observable

Semi-observable

Single Agent

Multi Agent

3. Problem Solving by Searching

9 hrs

3.1. Definition, Problem as a state space search, Problem formulation, Well-defined problems

3.2. Solving Problems by Searching, Search Strategies, Performance evaluation of search techniques

3.3. Uninformed Search

Depth First Search

Breadth First Search

Depth Limited Search

Iterative Deepening Search

Bidirectional Search

3.4. Informed Search

Greedy Best first search

A* search

Hill Climbing

Simulated Annealing

3.5. Game playing, Adversarial search techniques, Mini-max Search, Alpha-Beta Pruning

3.6. Constraint Satisfaction Problems

4. Knowledge Representation

14 hrs

4.1. Definition and importance of Knowledge, Issues in Knowledge Representation, Knowledge Representation Systems, Properties of Knowledge Representation Systems

4.2. Types of Knowledge Representation Systems

Semantic Nets

Frames

Conceptual Dependencies

Scripts

Rule Based Systems

Propositional Logic

Predicate Logic

4.3. Propositional Logic (PL)

Syntax, Semantics, Formal logic-connectives, truth tables

Tautology, validity, well-formed-formula

Inference using Resolution, Backward Chaining and Forward Chaining

4.4. Predicate Logic

FOPL, Syntax, Semantics, Quantification

Inference with FOPL, Unification and lifting, Inference using resolution

4.5. Handling Uncertain Knowledge

Radom Variables, Prior and Posterior Probability

Inference using Full Joint Distribution, Bayes' Rule and its use

Bayesian Networks, Reasoning in Belief Networks

4.6. Fuzzy Logic

5. Machine Learning

9 hrs

5.1. Introduction to Machine Learning, Concepts of Learning, Supervised, Unsupervised and Reinforcement Learning

5.2. Statistical-based Learning: Naive Bayes Model

5.3. Learning by Genetic Algorithm

5.4. Learning with Neural Networks

Introduction, Biological Neural Networks Vs. Artificial Neural Networks, Mathematical Model of ANN

Types of ANN

Application of Artificial Neural Networks

Learning by Training ANN, Supervised vs. Unsupervised Learning, Hebbian Learning, Perceptron Learning, Back-propagation Learning

6. Applications of AI

6 hrs

6.1. Expert Systems, Development of Expert Systems

6.2. Natural Language Processing

Natural Language Understanding

Natural Language Generation

Steps of Natural Language Processing

6.3. Machine Vision Concepts

6.4. Robotics

Laboratory Works

The laboratory work consists of design and implementation of intelligent agents and expert systems, searching techniques, knowledge representation systems and machine learning techniques. Students are also advised to implement Neural Networks, Genetic Algorithms for solving practical problems of AI. Students are advised to use LISP, PROLOG, or any other high level language.

1.Design and implementation of intelligent agents and expert systems
2.Implementation of searching techniques
3.Implementation of knowledge representation systems
4.Implementation of machine learning techniques
5.Implementation of Neural Networks and Genetic Algorithms

Text Books

1.Stuart Russel and Peter Norvig, Artificial Intelligence A Modern Approach, Pearson

Reference Books

1.E. Rich, K. Knight, Shivashankar B. Nair, Artificial Intelligence, Tata McGraw Hill.
2.George F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Benjamin/Cummings Publication
3.D. W. Patterson, Artificial Intelligence and Expert Systems, Prentice Hall.
4.P. H. Winston, Artificial Intelligence, Addison Wesley.

Notes:

This syllabus follows the official B.Sc. CSIT curriculum of Tribhuvan University. In case of any doubt or revision, the university’s published syllabus shall be considered authoritative.

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