COMP 350 Syllabus for Artificial
Intelligence Spring
2002
Week 1: Introduction to AI and Languages
·
History
and Issues of AI
·
Applications
of AI
·
Criticisms
of AI
·
Pragmatic
Issues
·
Philosophical
Issues
·
Social
and Ethical Issues
·
History
of LISP: Common Lisp
·
Overview
of Mathematica 4.0
·
Numeric
and Symbolic Processing in Lisp vs. Mathematica
Week 2: Programming in Common Lisp and Mathematica
·
Lambda
Notation and Function Writing
·
Functions
and Scoping
·
Sequence
Control: Conditionals
·
Character
and String Processing
·
Recursion
vs. Iterative Constructs
Week 3: More Programming
·
Applicative
and Mapping Functions
·
Destructive
Modification of Structures
·
Property
and Association Lists
·
Input
and Output
·
Coding
Efficiency and Programming Guidelines
Week 4: Knowledge Representation and Pattern
Matching
·
Human
vs. Computer Memory Models
·
Predicate
Notation and Extensions
·
Semantic
Networks
·
Conceptual
Dependency
·
Scripts
·
Frame
Structures and Processing
·
Pattern
Matching and Binding
·
Project
Selection
Exam 1 (100 Points)
Week 5: Search
·
Search
Applications in AI
·
Unguided
Search Methods: Depth-First vs. Breadth-First
·
Heuristic
Search Methods: Best-First and A-Star
·
Competitive
Search Methods: Minimax and Alpha-Beta
Week 6: Natural Language Processing
·
Spoken
Language
·
Written
Language
·
Grammars
·
Language
Understanding
·
Language
Generation
Week 7: Vision
·
Images
and Stages of Visual Processing
·
Transforming
3-D Scenes into 2-D Images
·
Image
Processing and Arrays
·
Early
(Numeric) Processing
·
Late
(Symbolic) Processing
Weeks 8-9: Logic and Expert Systems
·
Logical
Inference: Deduction
·
Plausible
Inference: Induction and Abduction
·
The
Idea of an Expert System
·
Rule-Based
Systems
·
Forward
and Backward Chaining
·
Certainty
Factors
Exam 2 (100 Points)
Week 10-11: Problem Solving, Planning, and Robotics
·
Phases
of Problem Solving
·
Planning
Methods
·
Manipulators
·
Simple
Robot Programming
·
Sensors
·
Propelling
Mechanisms
·
Autonomous
Robots
Weeks 12-13: Learning and Neural Networks
·
Machine
Learning Models
·
Analogies
and Induction
·
Computer
vs. Brain Processing Models
·
Propagation
and Activation Functions
·
Neural
Network Concepts
·
Two-Layer
Models
·
Hidden-Layer
Models
Weeks 14-15: Other AI Approaches and Presentations
Exam 3 (100 Points)
Course Goals: To gain an understanding of the approaches and techniques of
artificial intelligence and to learn how to perform the necessary symbolic and
numeric computations by using an appropriate programming language such as
Common Lisp. This course will emphasize
independent and creative thinking.
Texts: Artificial
Intelligence with Common Lisp: Fundamentals of Symbolic and Numeric Processing,
by James L. Noyes, D. C. Heath, 1992. Mindware: An Introduction to the Philosophy
of Cognitive Science, by Andy Clark, Oxford University Press, 2001. Some recent journal readings will also be
included.
Instructor: J. L. Noyes, Science, Rm.
329B, 327-7858. Office hours are posted
on the door.
Meetings: Three meetings will be
conducted per week: MWF 11:30 a.m. to 12:30 p.m. in Room 321.
Assignments: Both programming and
non-programming assignments will be given on a regular basis. Unless otherwise specified, these should be
done independently. These will be worth
approximately 300 points. Assignments
will be accepted in class. They may
also be turned in to the Instructor’s office by 5:00pm on the day assigned with
no penalty. After that, up to 10% of
the total points possible will be DEDUCTED
per day late (including weekends). Assignments
will not be accepted after three (3) days unless there is some type of emergency
situation and special arrangements are made ahead of time. Late assignments should be slid under the
office door (or under the department door, if it is locked) - be sure the
Instructor’s name is on it. For some
assignments, collaboration may be permitted, for other assignments, it will
not. Each assignment will indicate if
collaboration is permitted. When
collaboration is allowed, it is to only involve students in our class. As stated above, if you receive assistance
from someone else, be sure that you then understand how to do it yourself and
can explain ALL of it. You may contact me to answer questions, but
no other type of collaboration is permitted.
Research Projects: One or two research projects with a presentation, some programming, and
a typewritten report will be required.
This will be worth approximately 200 points and scheduled sometime
during the last 2-3 weeks of class.
There will be no comprehensive final exam.
Exams: Three 1-hour 100-point
exams will be given at relatively equal intervals during the course. Exams are
typically (although not always) based upon what has been covered by lecture
notes, previous labs, assignments, handouts, and text material. Exams and possible quizzes CANNOT be taken later without a legitimately
excused absence, which must be given in ADVANCE
(e.g., death in the family, personal illness, class field trip, necessary
Witt-sponsored activity). This excuse
is to be e-mailed to the instructor (jnoyes@wittenberg.edu) as far in advance as possible.
Implementation Languages: This course will utilize Allegro Common Lisp,
Wolfram’s Mathematica, and Metrowerks C++.
Academic
Dishonesty: Academic
dishonesty of any kind on homework or exams is not acceptable. This includes, but is not limited to,
plagiarism or unauthorized collaboration with another individual on homework or
tests. At a minimum it will typically
result in a reduced score (typically 0) for all parties involved and it could
result in a failing grade for this course.
In addition, there may be other University sanctions. See your Student Handbook for additional details
regarding Academic dishonesty.