CPSC 320 - 500: Artificial Intelligence


Spring Semester, 1998
Time and place: T/Th 2:20 - 3:35 127B Zachary
Instructor: Dr. Frank Shipman
Office hours: HRBB 402B, T/Th 3:45 - 4:45 pm, or by appointment


DESCRIPTION OF COURSE

This class provides an introductory level survey of artificial intelligence, its history and techniques. This course will cover various searching and game-playing algorithms, logic and its use for inference, planning algorithms, methods for dealing with uncertainty, and some basics of knowledge engineering, machine learning and natural language processing. The course will include a brief introduction to programming in LISP.

PREREQUISITES

Students should have knowledge of algorithm design and analysis (CPSC 311) and have a basic understanding of discrete math and logic.

TEXTBOOKS

Artificial Intelligence: A Modern Approach, Stuart Russell & Peter Norvig
covering chapters 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 14, 18, 22

Paradigms of Artificial Intelligence Programming: Case Studies in Common LISP, Peter Norvig
covering chapters 1, 2, 3

For more information on LISP see the TAMU Lisp Primer or this list of LISP resources

Here are the HTML reference specifications for ANSI LISP and Common LISP.

GRADING

homeworks 30%
mid-term exam 35%
final exam 35%

HOMEWORKS

There will be a number of short assignments due in class. These assignments will take the form of short essays, written answers to questions, and programming assignments. All material turned in should be printed using a computer printer or typed except when noted otherwise (you may be allowed to turn in hand-written solutions to some homeworks).

Homework late policy: 10% is deducted from your grade for every day late up to a maximum of one week.

Programming Labs: done in LISP (KCL or Allegro) on machines in HRBB 209 or 214. It is your own responsibility to learn how to use LISP on these machines although optional evening seminars will be provided by the TA.

TEACHING ASSISTANT

Brent Cox
Office: HRBB 331
Office hours: 3-4 MWF, or by appointment
Office phone: 845-1481
Email: bac@tamu.edu or brentc@cs.tamu.edu (responses of a sort guaranteed within 36 hours)

APPROXIMATE SCHEDULE (SUBJECT TO CHANGE)

Jan. 20 Overview of class plans (class notes)

Jan. 22 Introduction to AI (Chapter 1) (class notes)

Jan. 27 Intelligent Agents (Chapter 2) (class notes)

Jan. 29 LISP (Lisp book, Chapter 1) ( class notes)
(first written assignment due)
(optional evening seminars on how to use LISP by TA in HRBB 209 starting at 6:30 pm, 7:00 pm, 7:30 pm)

Feb. 2
(optional evening seminars on how to use LISP by TA in HRBB 209 starting at 6:30 pm, 7:00 pm, 7:30 pm)

Feb. 3 LISP (Lisp book, Chapter 2)

Feb. 5 LISP (Lisp book, Chapter 3) ( class notes)

Feb. 10 Search (Chapter 3) ( class notes)

Feb. 12 Informed Search (Chapter 4) ( class notes)
(programming assignment on pattern matching due, hints)

Feb. 17 Game Playing (Chapter 5) ( class notes)

Feb. 19 Representation and Logic (Sections 6.1 - 6.3, p. 151-166) ( class notes)

Feb. 24 Guest lecture on Intelligent Agents & Intelligent Tutoring Systems

Feb. 26 Propositional Logic (Sections 6.4 - 6.6, p. 166-178) ( class notes)

Mar. 3 First-Order Logic (Sections 7.1 - 7.3, p. 185-201) ( class notes)
(programming assignment on search due)

Mar. 5 Logical Agents (Sections 7.4 - 7.10, p. 201-212) ( class notes)
(turn in written assignment on logic if you want it graded before exam.)

Mar. 10 Review ( sample exam)
(written assignment on logic due)

Mar. 12 Mid-Term Exam (in class)

Mar. 24 Inference (Sections 9.1 - 9.5, p. 265-277) ( class notes)

Mar. 26 Reasoning Systems (Sections 10.1, 10.5-10.9, p. 297-298, 313-328) ( class notes)

Mar. 31 Planning (Sections 11.1 - 11.4, p. 337-349) ( class notes)

Apr. 2 Planning (Sections 12.1, 13.1-13.3, p. 367-371, 392-407) ( class notes)

Apr. 7 Uncertainty (Chapter 14) ( class notes)

Apr. 9 Vision (Sections 24.1 - 24.4, p. 724-749) ( class notes)
(written assignment on inference, reasoning systems, and planning due)

Apr. 14 Learning (Sections 18.1 - 18.3, p. 525-540) ( class notes)

Apr. 16 Neural Networks (Sections 19.1 - 19.5, p. 563-587) ( class notes)

Apr. 21 Walk (use this time to work on written assignment and lab)

Apr. 23 Natural Language Processing (Sections 22.1 - 22.3, 22.8, 23.5-23.7) ( class notes)
(written assignment on uncertainty, vision, learning, and neural networks due)

Apr. 28 Discussion of Artificial Intelligence (Chapters 26 & 27)

Apr. 30 Review
(programming assignment on natural language processing due)