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University of Technology, JamaicaSchool of Computing & Information Technology

PROGRAMME: All Bachelor Degrees in the School of Computing

COURSE TITLE: Artificial Intelligence (AI)

DURATION: 45 hours Lectures & Tutorials + 45 hours lab

CREDIT VALUE: 4

PREREQUISITIES: Data Structures, Discrete Mathematics, Theory of 

Computation

COURSE DESCRIPTION:

This course is designed to introduce students to the introductory concepts of ArtificialIntelligence. It will introduce students to areas that will provide them with the necessary

framework for further research in the field. It will also introduce them to two of the most

commonly used Artificial Intelligence programming languages.

GENERAL OBJECTIVES:

Students should1. Understand the concepts of intelligent agents

2. Appreciate the importance of different search techniques

3. Recommend the appropriate programming languages for different AI problems

4. Apply the use of backtracking, and back propagation to solve given AI

 problems5. Appreciate the usefulness of Expert Systems and other categories of AT

 programmes

UNIT 1 Introduction ( 2 Lectures)

SPECIFIC OBJECTIVES

At the end of this unit students should be able to:1. Define the term “Artificial Intelligence”

2. Describe the four approaches to AI3. State the foundations of AI

4. Describe the History of AI

5. Define an intelligent agent6. Describe the four structures of intelligent agents

7. Choose appropriate environments for different agents

Content

• Definition of Artificial Intelligence

• Approaches to Artificial Intelligence Turing Test

Cognitive Model

Laws of Thought

Rational Agent

• Foundations of Artificial Intelligence

• History of Artificial Intelligence

• Intelligent Agents

Structure

Environment

Autonomy

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Unit Outcome: Students should be able to state the appropriate environment and

corresponding intelligent agent for a given situation

Unit 2 Problem Solving (2 Lectures)

SPECIFIC OBJECTIVESAt the end of this unit students should be able to:

1. Use different search techniques to solve problems

2. Compare searches based on completeness and optimality3. Describe the characteristics of a problem

4. Define a problem

Content

• Problem Description

• Search Strategies

Uniformed Searches

Breadth First Uniform-Cost

Depth First

Depth Limited

Iterative Deepening

Bi-directional

Informed (Heuristic) Searches

Best First

Hill Climbing

Constraint Satisfaction

Simulated Annealing

Mean-Ends Analysis

Unit Outcome: Students should be able to recommend appropriate search

techniques for different problems.

Unit 3 Knowledge and Reasoning (3 Lectures)

SPECIFIC OBJECTIVES

At the end of this unit students should be able to:

1. Determine solutions using the “binding” approach2. Write facts in first order logics

3. Prove conclusions using first order logics

4. Explain the difference between predicate and prepositional logics

Content

• Knowledge based agents

• Knowledge Representation

Scripts

Semantics

Semantic Nets

Frames• First Order Predicate Logics

Syntax and semantics

Quantifiers

Inference in First Order 

Binding

Forward and Backward Chaining

Unit Outcome: Students should be able to prove whether a conclusion is possible

based on using inference in first order logics.

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Unit 4 Uncertainty (2 Lectures)

SPECIFIC OBJECTIVES

At the end of this unit students should be able to:

1. Define uncertainty2. State the axioms of probability

3. Calculate uncertainty using Bayes’ Rule.

Content

• Types of Probability

• The Axioms of Probability

• The Importance of Uncertainty

• Bayes’ Rule

• Bayesian Networks

Unit Outcome: Students should be able to calculate the uncertainty of any givensolution for a problem

Unit 5 Expert Systems (2 Lectures)

SPECIFIC OBJECTIVES

At the end this unit students should be able to:

1. Define an expert system2. Describe the process of building a knowledge base

3. Describe the different problems facing expert systems

4. State the importance of explanations.

Content

• Structure of an Expert System

Knowledge Acquisition

Knowledge Representation

Explanation

• Expert System Problems

Unit Outcome: Students should be able to build a simple expert system.

Unit 6 Learning and Communication (4 Lectures)

SPECIFIC OBJECTIVESAt the end of this unit students should be able to:

1. Define the term “machine learning”

2. Compare a neural network to a human brain3. Discuss the process of learning in neural networks

4. List the types of communication agents

5. Construct parse trees and sentences from context free grammar 

Content

• Learning

Supervised Learning

Reinforcement Learning, Sequential Decision making

Observation

 Neural Networks

Perceptrons

Back propagation learning

Applications

• Communication

 Natural Language

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Types of Communication agents

Context Free Grammar 

UNIT Outcome: Students should be able to show how a computer learning using

neural networks and how it can communicate using language

Unit 7 AI: the Future (1 Lecture)

SPECIFIC OBJECTIVES

At the end of this unit students should be able to:

1. State what Artificial Intelligence has already achieved for humanity2. Discuss difference possibilities for the future

Content

• AI at Present• The Possible Futures

UNIT Outcome: Students should be able to speculate possible outcome of the filed of 

Artificial Intelligence.

INSTRUCTIONAL APPROACHES

Lectures and Tutorial

 NotesLab sessionsPractical Assignment

ASSESSMENT PROCEDURES

2 Tutorial Tests 20%

2 Practical Tests 20%Final Practical Examination 20%

Final Theory Examination 40%

BREAKDOWN OF HOURS

Lecture 30 hoursTutorial 15 hours

Lab 45 hours

TEXT BOOKS

Recommended: Artificial Intelligence: A Modern Approach by Stuart Russell and

Peter Norvig

Supplementary:  Artificial Intelligence: Structures and Strategies for Complex

Problem Solving by George F. Luger and Williams A Stubblefield

Artificial Intelligence by Elaine Rich and Kevin Knight

Artificial Intelligence in Business: Expert Systems b y Paul Haron

and David King

Artificial Intelligence: Theory and Practice by Dean, Thomas,

Allen, James & Aloimonos, Yiannis

 

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PROLOG: Programming for Artificial Intelligence by Ivan Bratko

SYLLABUS WRITERS

Felix Oluwole AkinladejoRevised by Janett Williams

DATE OF LAST REVISION

May 22, 2006

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