SFU                       Computing Science                       05-1
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CMPT 882-3 G1"    Special Topics:Artificial Intelligence - An
                  Introduction to Knowledge Representation
 Instructor: J. Delgrande                    SFU Burnaby

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OBJECTIVE/DESCRIPTION:

Knowledge representation is the area of Artificial Intelligence concerned
with how knowledge can be represented symbolically and manipulated by
reasoning programs.  This area addresses fundamental problems dealing with
the design of languages for reprsenting knowledge, the formal
interpretation of these languages, and the design of computational
mechanisms for making inferences.  In this approach then, the problem of
reasoning with knowledge is on par with the representation of this
knowledge.  Since much of AI requires the specification of a large body of
domain-specific knowledge, this area lies at the core of AI.

TOPICS:

   o Topics will cover (most of) the following:
   o 1.  introduction
   o 2.  first-order logic
   o 3.  expressing knowledge
   o 4.  full clausal logic
   o 5.  Horn clause logic
   o 6.  procedural representations
   o 7.  production systems
   o 8.  frames
   o 9.  description logics
   o 10.  inheritance networks
   o 11.  defaults
   o 12.  probabilities
   o 13.  abductive explanation
   o 14.  action
   o 15.  planning
   o 16.  expressiveness / tractability
   o As well, specific topics will be introduced in the latter part of the
     course as time allows.  Intended additional topics include answer set
     programming (or extended logic programs), and issues in revising and
     merging knowledge bases.

GRADING:

The exact marking scheme will be decided in the first week of class in
consultation with students in the course.  Tentatively, the scheme is:  1.
4 Assignments worth 10% apiece.  Most will be pencil-and-paper assignments,
but there may be some programming.  Students will select 4 assignments from
a set of 8-10.  2.  20% class participation.  Since the class will be run
using a seminar-style format, credit will be given for preparing for class.
3.  30% course project, to be decided on by the student in consultation
with the instructor The exact marking scheme will be decided in the first
week of class in consultation with students in the course.  Tentatively,
the scheme is:  1.  4 Assignments worth 15% apiece.  Most will be
pencil-and-paper assignments, but there will be some programming 2.  20% -
midterm test.  3.  20% - final exam.

Students must attain an overall passing grade on the weighted average of
exams in the course in order to obtain a clear pass (C or better).

TEXTBOOKS:

   o Draft book by Brachman and Levesque.  To be distributed.  , , ,

REFERENCES:

   o Essentials of Artificial Intelligence, Matt Ginsberg, Morgan Kaufman,
     1993
   o Artificial Intelligence:  A modern approach, Stuart Russell and Peter
     Norvig, Prentice Hall,
   o Nonmonotonic reasoning, Grigoris Antoniou, MIT Press, 1997
   o Others as supplied by the instructor, , ,

PREREQUISITES/COREQUISITES:

Previous AI course, or approval of the instructor.

Distributed:  November 1, 2004

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