MCIS 670 - Artificial Intelligence and Expert Systems

MCIS 670 Artificial Intelligence (3 credits)

Fall 2003 (September 22 - December 12, 2003) /Online

Instructor

Dr. James Cannady
cannady@nova.edu
(954) 262-2085

Course Description

Includes an introduction to artificial intelligence as well as historical and current trends and characterization of knowledge-based systems. Search, logic and deduction, knowledge representation, production systems, and expert systems will be examined. Additional areas include architecture of expert systems and criteria for selecting expert system shells, such as end-user interface, developer interface, system interface, inference engine, knowledge base, and data interface.

Course Materials

Required Textbook: Russell & Norvig. Artificial Intelligence: A Modern Approach (2nd Edition). 2002.

Exit Competencies

Get an overview of artificial intelligence (AI) principles and approaches.
Develop a basic understanding of the building blocks of AI as presented in terms of intelligent agents: Search, Knowledge representation, inference, logic, learning.
Develop a brief overview of AI applications: Expert Systems and Planners.
Enable the student to follow AI literature with the ability to go on to independent work in the field.

Course Outline

The course material has been divided into five modules. The modules are designed to cover each of the major areas of Artificial Intelligence by including readings from the text, problems from the text, practical exercises, and supplemental readings.

Date	Assignment	Deliverable
09/22/03-10/05/03	Module 1: Introduction to Artificial Intelligence	Module 1 (due 10/05/03)
10/06-19/03	Module 2: Searching	Module 2 (due 10/19/03)
10/12/03	Project Proposal	Project Proposal (due 10/12/03)
10/20/03-11/02/03	Module 3: Machine Learning	Module 3 (due 11/02/03)
11/03-16/03	Module 4: Knowledge-based Systems	Module 4 (due 11/16/03)
11/17-30/03	Module 5: Reasoning with Uncertainty	Module 5 (due 11/30/03)
12/02-10/03	Final Exam	Final Exam (due 12/10/03)
12/12/03	Course Project	Project (due 12/12/03)

Module 1: Introduction to Artificial Intelligence

Assignment	Description	Points
Text Reading	Chapter 1	-
Text Problems	1, 3, 7, 9	3
Article Summary	Write a summary of Alan Turing's AI paper based on the established guidelines.	4
Practical Exercise	None	3

Module 2: Searching

Assignment	Description	Points
Text Reading	Chapter 3 & 4	-
Text Problems	Chapter 3: 1, 2 / Chapter 4: 1	3
Article Summary	Choose an article from a journal or conference proceedings on the topic of search strategies (e.g, depth-first, breadth-first, etc.). Write a summary on the article based on the established guidelines.	4
Practical Exercise	Run each of the demos of Uninformed Search Methods and the Informed Search Methods on the Virtual Lecture Project website	3

Module 3: Machine Learning

Assignment	Description	Points
Text Reading	Chapter 18 & 20	-
Text Problems	Chapter 18: 1, 2, 3 / Chapter 20: none	3
Article Summary	Choose an article from a journal or conference proceedings on the topic of machine learning. Write a summary on the article based on the established guidelines.	4
Practical Exercise	Download a freeware neural network package (check here or here for examples). After installing the software run the example data file that is included with the software. Take a screen shot of the results and paste the screen shot into a Word document.	3

Module 4: Knowledge-based Systems

Assignment	Description	Points
Text Reading	Chapter 10 & 19	-
Text Problems	Chapter 10: 1 / Chapter 19: 2	3
Article Summary	Choose an article from a journal or conference proceedings on the topic of expert systems. Write a summary on the article based on the established guidelines.	4
Practical Exercise	Run the available expert system demos on the Acquired Intelligence website. Save a screen shot of each and save in a Word file.	3

Module 5: Reasoning with Uncertainty

Assignment	Description	Points
Text Reading	Chapter 13 & 14	-
Text Problems	Chapter 13: 6 / Chapter 14: 1	3
Article Summary	Choose an article from a journal or conference proceedings on the topic of Bayesian reasoning. Write a summary on the article based on the established guidelines.	4
Practical Exercise	Download a freeware Bayesian network package. After installing the software run the example data file that is included with the software. Take a screen shot of the results and paste the screen shot into a Word document.	3

Course Project

The purpose of the course project is for the student to demonstrate an understanding of the various AI techniques and concepts. The student will develop a software application that demonstrates the use of one of the AI concepts covered in the course. All programming must be done in Java. The project must be a standalone application developed using Java 2 and not a Java applet or servlet.

Project Deliverables

Project Plan - The project plan will be limited to one page and will include a description of the proposed application, including the AI technique(s) that will be used, and a description of the application process (i.e. what it will do ). The project plan will be reviewed by the instructor and comments and recommended modifications will be provided as necessary. The project plan must be approved by the instructor before the student begins work on the actual software application. The project plan is worth 5% of a student’s final grade.

Project Report - The final deliverable will include a copy of the well-documented source code used in the application and a one page overview of the application that describes the AI techniques used, the application process, and the results provided by the application. The project application is worth 20% of a student’s final grade.

Instruction Methods and Tools

Questions and comments for the instructor should be submitted via e-mail. Any questions that are relevant to the class as a whole may be forwarded to the other students in the class by the instructor unless the student specifically requests that the question or comment remain in confidence.
All class assignments must be submitted via ESET. The modules should be submitted as a single zip file (all assignments included in the single zip file). Other deliverables may be submitted in PDF, ZIP, ASCII text, postscript, and Microsoft Word. You must submit each assignment to the correct location within ESET.
Scheduled online class discussions may be conducted utilizing WebCT. While students are not required to participate in any discussions you are encouraged to attend any online discussions conducted in the course.

Examination

The student will complete a comprehensive final examination during the final two weeks of the semester. Students may use any available materials in completing the examination, but all sources must be properly cited in the text and correctly referenced in a bibliography at the end of the document.

Grading Criteria

Achievement of the course objectives by student will be assessed using the following:

Project Plan	5%
Module 1	10%
Module 2	10%
Module 3	10%
Module 4	10%
Module 5	10%
Course Project	20%
Final Exam	25%

A	94 - 100
A-	90 - 93
B+	87 - 89
B	84 - 86
B-	80 - 83
C+	77 - 79
C	74 - 76
C-	70 -73
F	<70

Course Policies

All GSCIS course policies will be strictly enforced.
The student must notify the instructor of any circumstances that would interfere with the completion of an assignment prior to the due date ( NOTE: No late assignments will be accepted without the prior permission of the instructor).
Students are encouraged to discuss and share information pertaining to this course. However, all submitted work must be original and must be your own.
Programming is the student’s responsibility. I will not debug or offer remedial help with the programming in this course.
The final grade in this class will be based on all the material that is received by the last day of the term (December 12, 2003).