Fall 2019 - CMPT 721 G100

Knowledge Representation and Reasoning (3)

Class Number: 9018

Delivery Method: In Person


  • Course Times + Location:

    Mo, We, Fr 2:30 PM – 3:20 PM
    AQ 3159, Burnaby

  • Exam Times + Location:

    Dec 11, 2019
    12:00 PM – 3:00 PM
    AQ 3003, Burnaby

  • Instructor:

    James Delgrande
    1 778 782-4335
  • Prerequisites:

    CMPT 310/710 recommended. Cross-listed course with CMPT 411.



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


This course is cross-listed with CMPT 411

The area of Knowledge Representation and Reasoning is primarily concerned with encoding general world knowledge symbolically, in a form suitable for automated reasoning. This course will focus on central KRR methodologies, giving equal time to representational issues and reasoning issues.


  • Introduction : What do we mean by knowledge representation and why is it interesting?
  • Logic: expressing knowledge, first-order logic, Horn clause logic
  • Production systems (rule-based systems)
  • Description Logics
  • Defaults
  • Probabilities and uncertain reasoning
  • Diagnosis and abductive explanation
  • Reasoning about action
  • Planning
  • Expressiveness and tractability



The exact marking scheme will be decided in the first week of class in consultation with students in the course. Tentatively, four assignments and a midterm test and a 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).



Reference Books

  • Essentials of Artificial Intelligence, Matt Ginsberg, Elsevier Science & Technology Books , 1993, 9781558602212
  • Artificial Intelligence: A Modern Approach (3rd Edition), Stuart Russell and Peter Norvig, Prentice Hall,, 2009, 9780136042594


Knowledge Representation and Reasoning,
R. Brachman and H. Levesque,
Elsevier Science, 2004

This text is available online. As well, it is between "required" and "recommended"
ISBN: 9781558609327

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html