CSCE 580: ARTIFICIAL INTELLIGENCE
Catalog Course Description:
580—Artificial
Intelligence.
(3) (Prereq: CSCE 350) Heuristic problem solving, theorem-proving techniques,
and knowledge representation, including the use of appropriate programming
languages and tools.
Prerequisites By Topic:
Programming skills
Data structures and algorithms
Textbook and Other Required
Material:
Artificial
Intelligence, Third Edition, P. H. Winston, Addison-Wesley Publishing Company, 1992 (or Artificial Intelligence: A Modern Approach,
S. Russell and P. Norvig, Prentice Hall, 1995).
LISP, 3rd Edition, P. H. Winston
and B. K. P. Horn, Addison-Wesley Publishing Company, 1988.
CLIPS Manuals (online)
Computing Platform: Windows 2000, LISP, CLIPS
Course Objectives: {Assessment Methods Shown in
Braces}
1. Analyze and understand
software agents {homework, project}
2. Use the LISP programming language,
especially for recursive functions {tests, homework, project};
3. Converting net search to
tree search {tests, homework};
4. Perform depth-first,
breadth-first, and hill climbing search from a starting node to a goal node
{tests, homework};
5. Determine optimal search
paths using the A* search algorithm {tests, homework, project};
6. Represent knowledge in
predicate calculus form {tests, homework};
7. Use resolution for theorem
proving {tests, homework};
8. Represent knowledge in rule
form use the and use the CLIPS/JESS rule-based system {homework, project}.
Topics Covered:
1. Software agents
2. LISP programming
3. Net search
4. Optimal search
5. Propositional and predicate logic
6. Rule-based representation
7. Rule-based inference engines (CLIPS/JESS)
Syllabus
Flexibility: High. The instructor
selects the text and topics.
Relationship of Course to
Program Outcomes:
The
contribution of each course objective to meeting the program outcomes is
indicated with the following scale: 3 major contributor, 2 = moderate contributor, 1 = minor contributor. Blank if not related.
|
|
Program Outcomes |
||||||||||
|
1. Logic
& Math |
2. Computing
Fundamentals |
3. Apply
Computing Principles |
4. Work on
teams |
5.
Communicate Effectively |
6. Liberal
arts & Soc. Sciences |
7. Basic
Science and Lab Procedures |
8. Learn New
Tools & Processes |
9. Employed
upon Graduation |
10.
Application Area |
11. Electronics
and Digital Sys Design |
|
|
1. Software agents |
|
|
3 |
|
|
|
|
|
2 |
|
|
|
2. Use LISP |
|
|
3 |
|
|
|
|
|
1 |
|
|
|
3. Net search and tree search |
|
2 |
3 |
|
|
|
|
|
1 |
|
|
|
4. Depth-first, breadth-first, and hill climbing searches |
|
2 |
3 |
|
|
|
|
|
2 |
|
|
|
5. Optimal search |
|
3 |
|
|
|
|
|
|
|
|
|
|
6. Knowledge representation using predicate calculus |
|
2 |
|
|
|
|
|
2 |
|
|
|
|
7. Resolution theorem proving |
3 |
|
|
|
|
|
|
|
|
|
|
|
8. Rule-based knowledge representation (CLIPS/JESS) |
|
|
3 |
|
|
|
|
|
|
|
|
Estimated CSAB
Category Content:
Algorithms: 1.0
Data (Knowledge)
Structures: 1.0
Software Design: 0.5
Concepts of
Programming Languages 0.5
Organization and
Architecture 0
Oral
and Written Communication:
Oral presentation and written report on
project
Social
and Ethical Issues: none
Theoretical
Content:
Logic, inferencing, analysis of
algorithms
Analysis
and Design:
Implementation of project
Class/Laboratory
Schedule:
Lecture: 3 periods of 50 minutes or 2
periods of 75 minutes per week
Difference
between Undergraduate and Graduate Work:
Students
enrolled for graduate credit are required to complete a more demanding project
and are evaluated on a more rigorous grading scale than undergraduate students
to justify the receipt of graduate credit for this course.
Course
Coordinator: Larry Stephens
Modification
and Approval History:
Initial description, April 1999
Revised, March 2001
Revised July 2002 to include statement
on graduate work