ANNOUNCEMENTS(New ones will be added to the top as they arrive.)

·        Here are the final results. You can see your papers ONLY on June 15, between 15:30 and 16:00.

Student ID

final

2004720363

80

2004720093

69

2003800831

29

2000103760

78

2004720717

69

2004800045

79

 

·        Here are the presentations of Gerçeker, Kafalı, Ögat, Sönmez, Kutlubay, and Yücel.

·        Here are the midterm results:

Student ID

midterm

2004720363

57

2004720093

30

2003800831

22

2000103760

83

2004720717

69

2004800045

87

 

·        Here is the order of presentations for the first week of presentations: (Format: Presenter/paper number) Gerçeker/5, Yücel/1, Kafalı/7, Ögat/4. Your presentation should not be shorter than 30 minutes and longer than 45 minutes.

·        There will be no class on May 13, everybody is invited to the DNA Computation Conference.

·        The midterm exam is on May 6!

·        Here are the papers that you will present in May. (Contrary to standard rules of reference, only the numbers of the start pages are indicated.) The selection process works as follows: The first person who emails to me saying that he wants paper X gets paper X if paper X has not been assigned to anybody else by then. Presentations are made in the chronological order in which papers were assigned. I expect you to thoroughly understand your paper, and to present it so nicely during your 45-minute period that everybody else understands it as well. You may consult me if you have problems in understanding the papers. In the final exam, there may be questions to check your knowledge about papers presented by other people. You are supposed to prepare PowerPoint presentation files and submit them to me (at the time of your presentation at the latest) so that I can publish them on this webpage. (Note that some presentations about these papers may exist in the webpage of last year’s version of this course, and you may if you wish use them as a basis, but some of them contained some errors, and I will check if you noticed and corrected those errors if you use the old ppt’s.)

 

 

1. Model-Based Systems in the Automotive Industry
Peter Struss, Chris Price. AI Magazine. Winter 2004. Vol. 24, Iss. 4; p. 17

 

2. Qualitative Modeling in Education
Bert Bredeweg, Ken Forbus. AI Magazine. Winter 2004. Vol. 24, Iss. 4; p. 35

 

3. Qualitative Spatial Reasoning: Extracting and Reasoning with Spatial Aggregates
Chris Bailey-Kellogg, Feng Zhao. AI Magazine. Winter 2004. Vol. 24, Iss. 4; p. 47

 

4. Model-Based Programming of Fault-Aware Systems
Brian C Williams, Michael D Ingham, Seung Chung, Paul Elliott, et al. AI Magazine. Winter 2004. Vol. 24, Iss. 4; p. 61

 

5. Qualitative Reasoning about Population and Community Ecology
Paulo Salles, Bert Bredeweg. AI Magazine. Winter 2004. Vol. 24, Iss. 4; p. 77

 

6. Mathematical Foundations of Qualitative Reasoning
Louise Trave-Massuyes, Liliana Ironi, Philippe Dague. AI Magazine. Winter 2004. Vol. 24, Iss. 4; p. 91

 

7. Learning Qualitative Models
Ivan Bratko, Dorian Suc. AI Magazine. Winter 2004. Vol. 24, Iss. 4; p. 107

 

8. Model-Based Computing for Design and Control of Reconfigurable Systems
Markus P J Fromherz, Daniel G Bobrow, Johan de Kleer. AI Magazine. Winter 2004. Vol. 24, Iss. 4; p. 120

 

For understanding the last eight papers, it may also be a good idea to read the introduction: “Current Topics in Qualitative Reasoning
Bert Bredeweg, Peter Struss. AI Magazine. La Canada: Winter 2004. Vol. 24, Iss. 4; p. 13”

 

·        It seems that room Z09 has moved to ETA306 (in the same building, third floor), so don't get lost when looking for the first meeting of CmpE560.

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CmpE 560 Qualitative Reasoning

 

 

 

Catalog Data:

The need for reasoning with incomplete information. The qualitative representation: arithmetic and algebraic issues. Qualitative differential equations. Qualitative modeling and simulation. Qualitative variants of reasoning tasks like system identification, postdiction and comparative analysis.

 

Textbook:

Benjamin Kuipers, Qualitative Reasoning, MIT Press, 1994

Instructor:

Cem Say

 

 

 

 

Prerequisites:

(Any AI course + Math 202 or an equivalent differential equations course) or (consent of the instructor)

 

 

Topics:

Types of knowledge incompleteness

Modeling continuous change

Qualitative representation

Qualitative simulation

Semi-quantitative reasoning

Prediction and postdiction

Automatic modeling and qualitative system identification

Order of magnitude reasoning