CIS 630: Survey of Artificial Intelligence I
Description
A survey of the basic concepts and techniques of problem solving paradigms
and knowledge representation schemes in Artificial Intelligence (AI).
Level, Credits, Class Time Distribution, Prerequisites
| Level |
Credits |
Class Time Distribution |
Prerequisites |
|
U G |
3 |
Three one-hour lectures |
CIS 222; Math 366 |
Quarters Offered, General Information, Exclusions, Cross-Listings, etc.
Objectives
-
Master using basic search techniques for problem solving, including systematic blind searches, heuristically guided searches, and optimal searches
-
Be familiar with using game-tree search methods and the requirements for expert-level game play
-
Be familiar with using logic and proof as a basis for knowledge representation and automated reasoning
-
Be familiar with using rule-based systems as a way of encoding knowledge for problem solving systems
-
Be familiar with using semantic nets and frame systems as knowledge-representation formalisms
-
Be exposed to problems in common sense reasoning and language understanding
-
Be exposed to integrated AI architectures as a platform for building AI systems
-
Be exposed to neural networks and the kinds of problem they solve
| Relationship to ABET Criterion 3 |
Relationship to CSE Program Objectives |
| a |
b |
c |
d |
e |
f |
g |
h |
i |
j |
k |
| XXX |
X |
XX |
|
XX |
X |
X |
|
X |
|
XX |
|
| 1a |
1b |
1c |
2a |
2b |
3a |
3b |
3c |
4a |
4b |
| XXX |
XX |
XX |
|
|
X |
|
X |
XX |
XX |
|
This course is a technical elective, not a required
course. Hence the relations to ABET Criterion 3 and to CSE
Program Objectives apply only to the extent that a student chooses
this course as one of his or her technical electives.
Texts
- Artificial Intelligence by P. H. Winston, third edition,
Addison-Wesley, 1992
Topics
| No. of Weeks | Topics |
|
1 |
Introduction; basic problem solving methods |
|
2 |
Search techniques and problem solving |
|
1 |
Game-playing techniques |
|
2 |
Knowledge represention using logic; automated proof techniques |
|
2 |
Rule-based systems |
|
1 |
Common sense reasoning; problems in natural language understanding |
|
1 |
AI architectures; learning systems |
Grading Plan
Programming
Projects |
30% |
| Homeworks | 10% |
| Midterm Exam | 25% |
| Final Exam | 35% |
Preparer Information and Date:
Syllabus prepared by Rick Lewis, last modified April 5, 1999.