COMPUTER SCIENCE 431
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
(AKA Computational Intelligence)
Quick links to this site:
Administrivia
Meeting times:
3:00 - 3:50 MTThF, Thompson 311
Final Exam
The final exam for this class will be a take-home final
distributed during the last class meeting (Tuesday, Dec. 5), and due
during the first half of the scheduled final exam period (Thursday,
Dec. 12, 4:00 - 5:00).
Textbooks
- Required:
- Russell, Stuart, and Norvig, Peter, Artificial
Intelligence: A Modern Approach (Prentice-Hall,
1995).
- We will follow the suggestion (on page ix) for a
one-semester introductory course.
- In addition, we will look at (on-line) documentation for
CLIPS and SNePS, and have the opportunity to read several
papers (which will be provided in the coursepack).
- Reading packet (coursepack - available during the second
week of lectures)
- Suggested: Any book on Common Lisp. I will give you the basics
in lecture, and the book on Common Lisp by Guy Steele is available
on the web at
matthews@ups.edu)
- Thompson 502
- Extension 3561
Office hours (tentative)
- 9:00 - 9:50 AM MTThF
- Or by appointment.
Click here for a schedule of my
fixed obligations.
- If you catch me at any other time, please feel free to drop
in. Messages sent through the system MAIL program are welcome, and
can be used to ask a question or to set up an appointment.
- Web page: go to http://www.math.ups.edu
and follow the links to my home page, or you can go directly to
http://www.math.ups.edu/faculty/matthews.html. Along with
several resources you will find useful is the home page for this
course (
this file) which will be used to store assignments, weekly
schedules (and schedules of past weeks), and copies of previous
exams.
Notes
Programming exercises will be graded on style and documentation as
well as correctness. Programs must include header documentation as
well as adequate internal documentation unless otherwise specified.
Late assignments will be accepted (with an increasing penalty) until
the graded exercise is returned to the class. All assignments turned
in must represent individual effort: work done by a committee cannot
be accepted except where a group effort is a clearly stated part of
the assignment. All students in Computer Science classes at the
University of Puget Sound are responsible for the material contained
in the document on academic honesty published by the Department of
Mathematics and Computer Science and included in the Academic
Handbook.
A minimum grade of 50% on exams and 50% on homework assignments is
a necessary (but not necessarily sufficient) condition for a passing
grade.
Finally, the last date for withdrawing from this class with an
automatic W is Monday, Sept. 25. In the event that we do not have an
hour exam before that date, I will assign a WP grade to any formal
withdrawals (i.e., completed using the necessary form and submitted
to the register's office) up to a week after the day that the first
hour exam is returned to the class. Of course, should you find
yourself in difficulty at any point in the semester, please make
arrangements to meet with me as quickly as possible.
Weekly Schedule:
Assignments
Exam Reviews
Syllabus
Introduction
Catalog Description
This course introduces the student to the techniques of artificial
intelligence using LISP or Prolog. The student is introduced to the
basic techniques of uninformed and informed (heuristic) search,
production systems, expert systems, neural networks, and to
techniques of knowledge representation and problem-solving.
Additional topics may include alpha-beta pruning in game trees,
computer models of mathematical reasoning, natural language
understanding, machine learning, and philosophical implications.
Prerequisites
CSci 261 and some form of discrete or formal mathematics.
Experience with the propositional and predicate logics is a useful
prerequisite for this course. A grade of C- is required in
prerequisite courses.
Method of Instruction
- Lectures and seminar discussions
- Demonstrations and work sessions
- Homework and exams
- Paper/project
Evaluation
Students will be evaluated on the basis of homework, exams
(including a comprehensive final), and a paper or project as
follows:
- Homework: 40% (Including programming exercises in LISP,
Prolog, and CLIPS)
- Exams: 50%. The final exam will have the weight of two hour
exams.
- Project/Paper: 10%
You will be asked to explore one of the topics discussed in the
class in greater depth through either a written research paper or a
significant programming project (which may include one of the AI
tools available here at UPS or on the web). This may include both
advance or additional reading. A detailed description of the
paper/project together with a schedule will be available on Friday,
Oct. 6, but a draft schedule can be found in the lecture
schedule.
Basic, core material
Percentages should be taken as approximate guidelines. Topics may
not be presented in the following order.
Philosophical issues (10%)
- Descartes, Turing and Searle
- The physical symbol system hypothesis
- GOFAI ("good old-fashioned AI") vs. connectionist
AI
Language basics (10%)
Introduction to the language to be used in the course (LISP or
Prolog or both).
Searching (20%)
- State spaces and representations
- Operators
- Uninformed search
- Cost functions
- Branch and bound
- Heuristic search
- A* algorithms.
- Game playing and alpha-beta pruning.
Knowledge Representation (20%)
- Survey of knowledge representation techniques, including
- Database systems
- Isa-hierarchies
- Semantic networks,
- Frames and scripts
- Production systems
- Predicate logic.
- Procedural techniques
- Characteristics and evaluation of KR techniques
Problem solving. (20%)
- Problem solving as search
- Problem reduction
- And-or trees
- Means-ends analysis
- Rule-based expert systems
- Decision making under uncertainty
- Non-monatomic logic
- Statistical reasoning
- Inference networks
- Fuzzy logic
- Case Based Reasoning
- Control issues
- Agendas
- Forward vs. backward chaining
Neural Networks and Genetic Programming (20%)
- Perceptrons and the perceptron learning theorem
- Multilayer feed-forward neural networks and
backpropagation.
- Hopfield networks
- Genetic programming
Additional topics
In addition to the "core" topics listed above, the course should
include several of the following topics, depending on the interest of
the instructor(s) and the class:
- Robotics
- Mechanical Theorem Proving
- Game playing.
- Natural Language Understanding
- Computer Vision.
- Machine Learning.
Draft Schedule
Note: Please note that, except for scheduled University
events and exam dates, the schedule of topics, readings, and
assignments is tentative. It may be necessary to change an
exam date: if that happens, I will give you at least a week's notice
and make alternate arrangements for students unable to take the exam
on the rescheduled date. Please inform me of any conflict between the
dates entered here and those in the catalog and course schedule. In
the event of any conflicts, the catalog and course schedule have the
final say.
Exam Schedule
- Exam 1: Friday, Sept. 29
- Exam 2: Tuesday, Oct. 31 (Please note change in
date)
- Exam 3: Friday, Dec. 1 (during last full week of
class)
- Final Exam: The final exam for this class will be a
take-home final distributed during the last class meeting
(Tuesday, Dec. 5), and due during the first half of the scheduled
final exam period (Thursday, Dec. 12, 4:00 - 5:00).
Paper/Project Schedule
- Discussion of paper/project: Monday, Oct. 9
- Paper/project proposals due: Tuesday, Oct. 17.
Proposals will be returned on the following Friday.
- Paper/project bibliography due: Monday, Oct. 30
- First draft due Monday, Nov. 20
- Final copy due: 5:00 PM Wednesday, Dec. 6
More details on the paper or project will be forthcoming Monday,
Oct. 9. Please note in advance, however, that substantial penalties
will be assessed for missed deadlines for the paper, including all
of the submittables described above.
Lecture Schedule
Once again, please note that this is a tentative (and
somewhat ambitious) schedule. Although we will keep to the dates for
exams and project/paper dates, the rest of the
schedule will probably change. Please consult the "this week" and
"next week" files available through my home page for current
details.
- Week 1: Monday, Aug. 28
- Topics: Introduction
- Reading: 1,2
- Other Notes:
- Week 2: Monday, Sept. 4
- Topics: LISP I
- Reading: Notes
- Other Notes:
- Monday is Labor Day (no classes)
- Coursepacks available at the bookstore some time this
week.
- Week 3: Monday, Sept. 11
- Topics: Searching
- Reading: 3,4 (skip 5)
- Other Notes:
- Week 4: Monday, Sept. 18
- Topics: Lisp II
- Reading: Notes
- Other Notes: Exam I will be next Friday
- Week 5: Monday, Sept. 25
- Topics: Logic
- Reading: 6, 7 (skip 8?)
- Other Notes:
- Week 6: Monday, Oct. 2
- Topics: Logic
- Reading: 9 (skip 10)
- Other Notes:
- Week 7: Monday, Oct. 9
- Topics: Expert Systems using CLIPS
- Reading: Handout and notes
- Other Notes:
- Midterm is Oct. 13 (no exam)
- Exam #2 will be next Friday
- Week 8: Monday, Oct. 16
- Topics: Planning
- Reading: 11, 13 (skip 12)
- Other Notes:
- Exam #2, rescheduled to Tuesday, Oct. 31
- Week 9: Monday, Oct. 23
- Topics: Probabilistic reasoning
- Reading: 14, 15 (skip 16, 17)
- Other Notes:
- Exam #2, rescheduled to Tuesday, Oct. 31
- Week 10: Monday, Oct. 30
- Topics:
- Catch-up and review
- Uncertainty in CLIPS
- Reading: As needed
- Other Notes:
- Exam #2, rescheduled to Tuesday, Oct. 31
- Week 11: Monday, Nov. 6
- Topics: Neural Networks
- Reading: 18, 19
- Other Notes:
- Week 12: Monday, Nov. 13
- Topics: Neural Networks
- Reading: 18, 19
- Other Notes:
- Week 13: Monday, Nov. 20
- Topics: NLP
- Reading: 22, 24 (skip 23)
- Other Notes:
- Exam #3 will be next week
- Thanksgiving vacation Thursday and Friday
- Week 14: Monday, Nov. 27
- Topics: Philosophy
- Reading: 26, 27
- Other Notes: Exam #3 will be Friday, Dec. 1. Please note
that this is in the last full week of class.
- Week 15: Monday, Dec. 4
- Topics: Catch-up and review
- Reading: As required
- Other Notes: Term paper due Wednesday, Dec. 6
- Week 17: Monday, Dec. 11: Final Exams
Week
- Final Exam: The final exam for this class
will be a take-home final distributed during the last class
meeting (Tuesday, Dec. 5), and due during the first half of the
scheduled final exam period (Thursday, Dec. 12, 4:00 -
5:00).