Artificial Intelligence

Course Title: Artificial Intelligence

Course No: BIT252

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 concepts of artificial intelligence. It includes the basics of artificial intelligence, 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 and to learn intelligent agents, identify AI problems and solve the problems using searching, design knowledge representation and expert systems, understand concepts of machine learning and natural language processing.

Course Contents

1. Introduction

3 hrs

1.1. Artificial Intelligence (AI)

1.2. History of AI

1.3. AI Perspectives

1.4. Turing Test

1.5. Foundations of AI

1.6. Scope of Symbolic AI

1.7. Applications of AI

2. Agents

5 hrs

2.1. Introduction of Agents

2.2. Configuration of Agents: PEAS description of Agents

2.3. Types of Agents

Simple Reflexive

Model Based

Goal Based

Utility Based

Learning Agent

2.4. Environment Types

Deterministic, Stochastic

Static, Dynamic

Observable, Semi-observable

Single Agent, Multi Agent

3. Problem Solving by Searching

10 hrs

3.1. Problem Solving

3.2. State Space Representation

3.3. Problem Formulation

3.4. Constraint Satisfaction Problems

3.5. Performance evaluation of search techniques

3.6. Uninformed Search

Depth First Search

Breadth First Search

Depth Limited Search

Iterative Deepening Search

Bidirectional Search

3.7. Informed Search

Greedy Best first search

A* search

Hill Climbing

3.8. Game playing

Adversarial search techniques

Mini-max Search

Alpha-Beta Pruning

3.9. Problem Decomposition

Goal Trees

4. Knowledge Representation

14 hrs

4.1. Knowledge

4.2. Knowledge Representation

4.3. Issues in Knowledge Representation

4.4. Knowledge Representation Systems

4.5. Properties of Knowledge Representation Systems

4.6. Logic Based: Propositional and Predicate

Propositional Logic: Syntax, Semantics, CNF Form, Inference using Resolution, Backward Chaining and Forward Chaining

Predicate Logic FOPL: Syntax, Semantics, Quantification, Inference with FOPL: Unification and Lifting, Inference using Resolution

4.7. Semantic Nets, Frames, Rule Based Systems, Scripts, Conceptual Dependency

4.8. Statistical Reasoning

Uncertain Knowledge, Random Variables, Prior and Posterior Probability, Bayes' Rule

Bayesian Networks, Reasoning in Belief Networks, Dempster-Shafer Theory

5. Neural Network

2 hrs

5.1. Introduction to Neural Networks

5.2. Mathematical Model of ANN

5.3. Designing a neuron

5.4. Types of ANN

Feed-forward, Recurrent

Single Layered, Multi-Layered

5.5. Learning Rule, Learning Rate

5.6. Application of Artificial Neural Networks

6. Machine Learning

5 hrs

6.1. Machine Learning

6.2. Concepts of Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

6.3. Learning by Analogy

6.4. Learning by Genetic Algorithm

6.5. Learning by Back-propagation

7. Expert System

3 hrs

7.1. Expert Systems

7.2. Architecture of Expert System

7.3. Development of Expert Systems

7.4. Applications of Expert Systems

8. Natural Language Processing

3 hrs

8.1. Natural Language Processing: Natural Language Understanding and Natural Language Generation

8.2. Steps in NLP

Lexical Analysis

Syntactic Analysis

Semantic Analysis

Discourse and Pragmatic Analysis

8.3. Ambiguities in NLP

Laboratory Works

The laboratory work consists of implementation of intelligent agents and expert systems, searching techniques, knowledge representation systems and machine learning techniques. Students are advised to use LISP, PROLOG, or any other high level language.

1.Implementation of Intelligent Agents
2.Implementation of Searching Techniques
3.Implementation of Knowledge Representation Systems
4.Implementation of Expert Systems
5.Implementation of Machine Learning Techniques

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.
5.Tutorials for LISP and PROLOG

Notes:

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

Artificial Intelligence

Artificial Intelligence

Select University

Select Program

TabFlux . Artificial Intelligence . TU . BIT