CPSC 433: Artifical Intelligence Winter 2006 
Instructor:
 Rob Kremer, ICT 748, email: kremer@cpsc.ucalgary.ca;  
Lectures:
 Tues/Thurs 14:0015:15 in ICT 114  
Office
hours:  Tues/Thurs 11:0012:00 and by appointment.  
Course
web site:  http://kremer.cpsc.ucalgary/courses/cpsc433/W2006/index.html  
Mail
list server:  cpsc433@cpsc.ucalgary.ca (also see how to subscribe by email, or just subscribe directly with SYMPA)  
Tutorials/Labs:


An examination of the objectives, key techniques and achievements of work on Artificial Intelligence in Computer Science.
Computer Science 313 and one of 349 or 449
Note that a basic understanding in logic is definitely required for this course (Philosophy 279 or 377 are prerequisites of CPSC 349 and 449, therefore they are not explicitly mentioned in the calendar)! Although we will introduce the basic concepts of how to process and solve problems described in logic in this course, knowing what logical formulas, propositions and calculi are and how a problem can be represented as a set of formulas is a must!
You might also be interested in looking at Prof. Denzinger's manuscript (containing the first two chapters of his upcoming book [translated from German]), which describes the search models according to the same onology as used in class.
The University policy on grading and related matters is described in the university calendar.
The course will have a Registrar's scheduled final examination and a midterm exam. These exams together constitute the exam component of the course and there is also an assignment component and a peer evaluation component. All three components have to be passed in order to pass the course, and both parts of the peer evaluation must be passed in order to pass that component. Even though the peer evaluation component has only a nominal mark associated with it, you may fail the entire course for failing to complete these tasks adequately.
The final grade will be calculated using the grade point equivalents of the individual grades achieved weighted by the percentages given later on this page. To get the final letter grade for the course, the weighted sum is converted back using the official University grade point equivalents. In order to deal with the grade A+ that unfortunately does not scale in the grade point equivalents, the following rule will apply: an A+ as final letter grade will be awarded to every student who has an A in both exams and in both components of the assignment component and in both components of the peer evaluation component.
As already described, we follow the usual midtermfinalscheme for exams. The weighting of the grades you achieve in these two exams is as follows:
Midterm  20%  
Final exam  30% 
Remember, you have to pass this component to pass the course. For example, this means that a D in the midterm and an F in the final is not sufficient!
For a detailed description of what you have to do, please refer to the assignment page. The following table describes the percentage with which the individual task grades will be weighted in the final grade for the course.
Paper presenting two solutions to the given problem  18%  
Implementation and demonstration of the selected solution  30% 
So, the assignment component accounts for 48 percent of your mark. Please note that both grades above are achieved by your team!
The peer evaluations are relatively simple to do compared with the other course components. You must submit a report to your TA and instructor (by email) containing a letter grade assessment of each (including yourself) of your group members' contributions to the current assignment together with a 1/3 page description of that person's contribution justifying your assessment. It is not acceptable to give everyone in your group an A or to give everyone in your group an F. A report that does not reflect the dynamics of the group will be considered a failure, which could cause you to fail the course. It is your responsibility to get to know your group members and know their contributions to the project.
The weighting in the final grade for the course is
Peer evaluation 1 (to be handed in just after the paper assignment)  1%  
Peer evaluation 2 (to be handed in just after the implementation and demonstration assignment)  1% 
So, the peer evaluation component accounts for only 2 percent of your mark, but you have to do both of them adequately in order to pass the course.
Why
the peer evaluation? The peer evaluation is set up to prevent freeriding (a
group member doing little or nothing and getting a good mark by taking advantage
of the efforts of the rest of the group). It will be used as follows: The TA and
instructor will use their own judgment and experience with the group as well as
the input from the peer evaluations to assess a "delta mark", which
will be applied to the group assignment. The delta mark will be a positive or
negative lettergrade value between 4 and +1, which will be added to the group
mark for each individual member. Therefore, if you are the group leader, make a huge contribution to the project, your group thinks you can walk on water, and your group project is assessed as a "B", you may be assessed a delta mark of +1  your mark for the project will be "A". On the other hand, if you could had done better on the project, your delta mark may be 0.7, and you'd get a group mark of "C+". Furthermore, if you really didn't help much at all on the project, you'd get a delta mark of 4, you'd get a "F" for the project and you'd automatically fail the entire course. Don't let that happen. :) 
Date 
Tuesday 14:0015:15  Thursday
14:0015:15  

Jan10/12 
Introduction, Structure
of an AI system, knowledge processing [ PPT]  
Jan17/19  
Jan24/26 
Search: Andtreebased
search [ PPT] 
Search: Ortreebased
search [ PPT]  
Jan31/02 
Search: Other models & Search
control issues [ PPT]  
Feb07/09 
Search: Search control issues & Knowledge
Representation (propositional logic) [ PPT] 
Discussion about the assignment & Knowledge Representation
(propositional logic) &  
Feb14/16  More discussion
about the assignment &Knowledge
Representation (first order logic) [ PPT] 
Knowledge Representation (first order logic)
 
Feb21/23 
Reading Week  No Lectures  
Feb28/02  Logic:
Firstorder logic  
Mar07/09  Midterm review
/ Logic: Other logics  Midterm  
Mar14/16 
Rulebased
Systems [ PPT]: Prolog 
Rulebased Systems: Mycin  
Mar21/23  Frames,
Semantic Nets [ PPT]  
Mar28/30 
Semantic Nets  
Apr04/06  
Apr11/13 
Last updated 20070829 6:28 