COSC 6368 --- Artificial Intelligence Spring 2006 ( Dr. Eick )

Purpose of this Website

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Basic Course Information

Instructor: Dr. Christoph F. Eick
office hours (589 PGH): TU noon-1p TH 4-5p
TA: ???
office: ??? PGH
e-mail: ??? officehours ( ):
class meets: TU/TH 10-11:30a in 200 PGH
cancelled classes:
makeup classes:
class room: 200 PGH

Course Materials

Required Text:
S. Russell and P. Norvig, Artificial Intelligence, A Modern Approach, Second Edition,
Prentice Hall/Allyn&Bacon, 2003,
Link to Textbook Homepage.

Optional books with relevant material:
N. Nilsson, Artificial Intelligence: A New Synthesis
Morgan Kaufmann, 1998, ISBN: 1-55860-467-7, $59.95
Call number: Q335.N495 1998

E. Rich and K. Knight, Artificial Intelligence, 2nd ed.
McGraw Hill Book Company, 1991, ISBN: 0-07-052263-4, $71.50
Call number: Q335.R53 1991

M.R. Genesereth and N. Nilsson, Logical Foundations of Artificial Intelligence
Morgan Kaufmann, 1987, ISBN: 0-934613-31-1, $61.95
Call number: Q335.G37 1988 and Q335.G37 1987

News COSC 6368 Spring 2006

Material Covered in COSC 6368

Artificial Intelligence(AI) resarch centers on the simulation of intelligence in computers. The class gives an introduction to Artificial Intelligence(AI), and surveys AI technologies, techniques, methodologies, and algorithms. In particular, the subfields of AI problem solving and heuristic search, logical reasoning, reasoning with uncertain knowledge, and machine learning/data mining will be covered in more depth by COSC 6368 (see Organization of our textbook for more details).

Course Organization


Students are expected to have the following background:

The prerequisites for the class are important, but only up to a point. The real prerequisite for this course is the ability to solve abstract problems, to understand nontrivial algorithms, and to have basic programming and system development skills. To some people these skills come by more easily, whereas others get them by taking the corresponding classes. If you feel that you have these skills, you can easily make up for the prerequisites on your own.

Course Assignment Topics and Assignments

Assignment1: Heuristic Search and Evolutionary Computing
Assignment2: Learning from Examples
Assignment3: Logical Reasoning and Reinforcement Learning
Assignment4: Probabilisic Reasoning and Belief Networks, Foundations of AI

2004 Assignment1 ( Solution Problems 2-4, Benchmark TSP Problem and Discussion of Cost Functions c1 and c3, Discussion of Different Approaches TSP-Problem, Results of Running Student Programs for the TSP Benchmark)
2004 Assignment2 ( Solution Sketch Problem 7, Solution Sketch Problem 11)
2004 Assignment3

Due Dates and Exam Dates 2006

ActivityDue DateWeight
Assignment1Sa., Feb. 18, 06 and Th. March 2 in class19 *'s
Assignment2Tu., March 21, 06 in class 11 *'s
Assignment3Tu. April 11, 06 in class17 *'s
Assignment4Th. April 27, 06 in class13 *'s
Exam1Th., Feb. 2318%
Exam2Th., April 618%
Final ExamMay ??. 2006 2-5p30%

Remark: weights are somewhat random and subject to change!

Class Transparencies

Here is some information concerning transparencies to be used in the lectures of COSC 6368 (the transparencies are listed approximately in the order in which they will be covered): The Russel transparencies can also be obtained by following the instructor link from the textbook link, and then clicking the slide link.

Textbook Coverage

One goal of this class is to give you a very up-to-date introduction to AI. To my best knowledge chapters 1, 4, 6, 8, 9, 14, 18, 20, and 27 of the Russel textbook will be covered indepth. Chapters 3, 10, and 26 will be partially covered by COSC 6368. If there is enough time left at the end of the semester, chapters 5 and 11 will be also covered. Additionally, a few journal articles and transparencies of the instructor will be used as teaching material, especially for the lectures that cover evolutionary programming, ontologies, and data mining. Moreover, frequently, examples will be discussed in the lectures that are not contained in the listed teaching material.


The course will have a midterm exam (scheduled for Tu., October ??) and a final exam (scheduled for Tu., December 14, 2004), 4 assignments (that contain paper&pencil-style questions, or require to solve particular problems using AI-tools, or require programming), and a paper walkthrough. Each student has to have a weighted average of 74.0 or higher in the exams of the course in order to receive a grade of "B-" or better for the course. Students will be responsible for material covered in the lectures and assigned in the readings. All homeworks and project reports are due at the date specified. No late submissions will be accepted after the due date. This policy will be strictly enforced. Course grades will be based on 37% final exam, 30% midterm exam, 33% will be allocated for non-exam activities (3% for paper walkthrough and 30% for the 4 assignments).

Translation number to letter grades:
A:100-90 A-:90-86 B+:86-82 B:82-77 B-:77-74 C+:74-70
C: 70-66 C-:66-62 D+:62-58 D:58-54 D-:54-50 F: 50-0

Only machine written solutions to homeworks and assignments are accepted (the only exception to this point are figures and complex formulas) in the assignments. Be aware of the fact that our only source of information is what you have turned in. If we are not capable to understand your solution, you will receive a low score. Moreover, students should not throw away returned assignments or tests.

Students may discuss course material and homeworks, but must take special care to discern the difference between collaborating in order to increase understanding of course materials and collaborating on the homework / course project itself. We encourage students to help each other understand course material to clarify the meaning of homework problems or to discuss problem-solving strategies, but it is not permissible for one student to help or be helped by another student in working through homework problems and in the course project. If, in discussing course materials and problems, students believe that their like-mindedness from such discussions could be construed as collaboration on their assignments, students must cite each other, briefly explaining the extent of their collaboration. Any assistance that is not given proper citation may be considered a violation of the Honor Code, and might result in obtaining a grade of F in the course, and in further prosecution.

Course Exams


The midterm will be given on Tuesday, October 26, 2002 during the regular class hours. Here is the Review List for the 2001 Midterm Exam. The exam will be "open textbooks".

Review Sheet for 2001 Midterm Exam
Grades for the 2002 midterm exam
2004 Midterm Exam with some Solutions

Final Exam

The final will be held Dec. 17, 2002 11a.

Review Sheet for 2002 Final Exam (Dec. 17, 2002)
COSC 6368 Final Exam Fall 1999 (in Word)
COSC 6368 Final Exam Fall 2001 (in Word)
Part 2 of Fall 1999 Qualifying Exam (in html)

2004 AI Qualifying Exam

The AI Qualifying Exam will consist of two parts: part1 with be the final exam of COSC 6368. Part2 will be an extra exam that will be given on Thurday, December 16, 2002, 10:30a in room PGH 350 Part2 of the qualifying exam will cover the following areas: Part2 will take approx. 80 minutes; Part1 of the qualifying exam has a weight of approximately 60% and Part2 has a weight of 40%. Both exams are open textbook and notes! The same paper was used in the 2002 AI QE. Obviously, the questions in the 2004 QE will be different, but looking at the 2002 questions might give you a feeling what to expect.

Communication with the teaching staff

We strongly encourage students to come to my office hours or to talk to me directly after class. If a homework clarification is posted after a student has completed an assignment, the student should contact us as soon as possible to check if the assumptions s/he made are going to be accepted.

Please do not e-mail us with grading questions. If you want us/me to explain why I took points off, you can talk to me/us during office hours and directly after class.

Material COSC 6368 Fall 1999

Midterm Exam Fall 1999
Problems Homework3+4 (RETE Problem corrected on Nov. 16, 9a, and problem 16 updated on Nov. 19)
C5.0 Tutorial (C5.0 is a decision tree machine learning and knowledge discovery tool --- probably, the most famuous one of the decision tree family)

Stanford Page with Additional Course Material

Assignment Problems Fall 2002

Assignment1 (Benchmark Problem 5)
Assignment2 ( Scores for the NBA Data Analysis Problem (#7)))
Assignment3 (Solution for problem 12 and 13a)
Assignment4 (Solution Problem 15b)

Assignment Scores Fall 2002 (reported scores for problem 14 and 15 are still subject to verification)

Projects and Assignments Fall 2001

Selected Solutions of 2001 Homework Problems

Assignment1: Heuristic Search Training Benchmark (contains 10 example problems to be used to test your program)

Assignment2: Machine Learning Fall 2001
Group Silver's Decision Tree Homepage (contains useful links and information for Assignment2)

Assignments 3+4
Group Gold's Beliefnetwork Page

last updated: January 13, 2005, 2:30p

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