CS2710 (ISSP 2160) Foundations of Artificial Intelligence  

CS 2710 (ISSP 2160)  Foundations of Artificial Intelligence

MW 11:30-12:45pm,  Room 5313 (Sennott Square), Fall 2010

Instructor: Dr. Diane Litman
5105 Sennott Square, x4-8838
741 LRDC, x4-1261
e-mail: litman_at_cs.pitt.edu
office hours: M 12:45-2:15 Sensq, Th 11:30-1 LRDC, and by appt.
TA: George Boulos
6150 Sennott Square
e-mail: gwf5_at_pitt.edu
office hours: W 12:45-2:15, F 1:30-3

Textbook: Stuart Russell, Peter Norvig. Artificial Intelligence.  A Modern Approach. Third Edition. Prentice Hall, 2010. (Note: this is a NEW edition of the textbook!!)

Schedule (tentative)  
Date  Topic(s)  Assignments Links
I Artificial Intelligence    
8/30 AI

Readings: RN 1 (pdf), 2 (pdf)

How to read a computer science research paper

How to read a research paper

ASSIGNED: Commentaries (due 8/31 and 9/1) ASSIGNED: Python tutorial (due 9/8)

Association for the Advancement of Artificial Intelligence (AAAI)

NY Times AI news

Tools for Learning AI

II Problem-solving    
9/1 Solving problems by searching

Readings: RN 3 (pdf)

DUE (by 8/31 11:59 PM): Commentary on Artificial Intelligence: The Next Twenty-Five Years (AI Magazine 2006)

DUE (by 9/1 11:59 PM): Leave a comment on Blackboard to sign up for (co-)leading one paper/commentary class discussion. The available topics/papers are indicated on this syllabus.

9/8 Informed search, Beyond classical search

Readings: RN 3.5-3.7, 4.1 (pdf)

DUE:Python Tutorial (submission instructions)

9/13 Informed search, Beyond classical search

Readings: continued

ASSIGNED: Homework 1 (programming)

9/15 Constraint satisfaction problems

Readings: RN 6.1-6.4 (pdf), A* review

DUE:Commentary on Real-Time Heuristic Search: First Results (2006 AAAI Classic Paper Honorable Mention) [Conn, Dapkus]

Constraints: An International Journal

Handbook of Constraint Programming

9/20 Adversarial search

Readings: RN 5.1-5.5 (pdf)

CSP to work on at home for class discussion play checkers with Chinook
III Knowledge, reasoning, and planning    
9/22 Logical Agents

Readings: RN 7 (pdf)

DUE: Commentary on The Game of Hex: An Automatic Theorem Proving Approach to Game Programming (2000 AAAI Outstanding Paper Award) [Jung, Walker]

ASSIGNED: Homework 2 (written); Solutions

Hexy plays Hex
9/27 Propositional logic

Readings: Pruning (review, chance), RN 7 (continued)

  Knowledge Engineering (comic)
9/29 Propositional logic

Readings: RN 7 (continued), resolution example (note proof is different than one derived in class)

Due: Homework 1

ASSIGNED: Homework 3 (programming)

10/4 First order logic

Readings: RN 8 (pdf)

FOL problem for class discussion  
10/6 Inference in FOL

Readings: RN 9 (pdf)

Due: Homework 2

ASSIGNED: Homework 4 (written); Solutions

10/12 Monday classes meet on Tuesday!    
10/12 Inference in FOL

Readings: continued

CNF/Resolution problems to work on at home for class discussion (solutions)

10/13 Classical planning

Readings: RN 10.1-10.3 (pdf)

Due: Homework 3

The International Conference on Automated Planning and Scheduling (ICAPS)
10/18 Classical planning

Readings: continued

10/20 Midterm Review Due: Homework 4

Sample Exams: 2006, 2009

10/25 Classical planning

Readings: RN 7.7.1, 7.7.4, 10.4-10.6 (pdf)

10/27 Midterm Exam
(closed-book, NO makeups)

Coverage: Chapters 1-9

11/1 Planning, Knowledge representation

Readings: RN 12 (ppt)

Background reading for assignments

ASSIGNED: Homework 5 (writing and peer review)

Principles of Knowledge Representation and Reasoning, Incorporated (KR, Inc.)

11/3 Knowledge representation

Readings: RN 12

DUE: Commentary on Searching for Common Sense: Populating Cyc(TM) from the Web. (Proc. AAAI 2005) [Anderson, Nelson]

ASSIGNED: Homework 6 (written and programming); Example Solution

Cyc Knowledge Base

IV Uncertain knowledge and reasoning    
11/8 Quantifying uncertainty

Readings: RN 13 (pdf)

11/10 Quantifying uncertainty

Readings: review (pdf), Wumpus (pdf),

DUE: Commentary on The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users (UAI 1998) [Hong] Association for Uncertainty in AI
11/15 Probabilistic reaasoning

Readings: RN 14.1-14.4.2 (pdf)

DUE: Homework 5 (First draft)

11/17 Probabilistic reaasoning

Readings: RN 14.3 (pdf)-14.4.2 (pdf)

DUE: Homework 6

ASSIGNED: Homework 7 (written and a little "programming"); Solution

JavaBayes, GeNIe/SMILE
11/22 Making simple decisions,
Learning from examples

Readings: RN 16.1-16.3.2 (pdf) 18.1-18.4 (pdf)

V Learning    
11/29 Learning from examples

Readings: continued, also selections from RN 18.10-18.11 (pdf)

DUE Homework 5 (Reviews) International Machine Learning Society


Netflix contest

Machine Learning Repository of data sets.

12/1 Learning from examples, NLP

Readings: continued (pdf)

DUE: Homework 7

DUE: Commentary on An Empiricial Comparison of Supervised Learning Algorithms (ICML 2006) [Liu, Nguyen]


VI Communicating, perceiving, and acting    
12/1 Natural Language Processing

Readings: RN 22 (pdf)

  Association for Computational Linguistics
12/6 Natural Language Processing

Readings: continued, RN 23.1-3 (intro)

DUE Commentary on Not All Seeds Are Equal: Measuring the Quality of Text Mining Seeds (NAACL/HLT 2010). [Friedberg]  
12/8 Natural Language for Communication

Readings: RN 23.1-3 (pdf)

DUE: Commentary on Dialog in the Open World: Platform and Applications (Outstanding paper award at ICMI-MLMI 2009) (videos) [Amraii]

DUE Homework 5 (Back Reviews, Final Version)

Last "Homework"

12/13 Final exam review DUE Homework 5 (Final Reviews)

Review notes; Sample exam

12/15 Final Exam
(closed book, NO makeups)

Coverage: All material since midterm

Note that for graduate courses there is no special schedule, so our exam is at the regular class time, regular place.


Course Description

This course will provide an introduction to the fundamental concepts and techniques underlying the construction of intelligent computer systems. Topics covered in the course include: problem solving and search, logic and knowledge representation, planning, uncertainty, and advanced topics.

Prerequisites: undergraduate level AI (CS 1571 or equivalent) or the permission of the instructor

Textbook: Stuart Russell, Peter Norvig. Artificial Intelligence.  A Modern Approach. Third Edition. Prentice Hall, 2010.
Note: The third edition of the book was published in 2010. There are significant changes as compared to the second (2002) edition of the book. Please make sure to obtain the 3rd edition (which was not used in previous years).

Additional readings: Links to research papers will be found under the Assignments section of the syllabus. The procedure for commenting on these papers is described here.

  • Readings/Peer Feedback  10 %
  • Homework assignments    45 %
  • Midterm                           20 %
  • Final                                 25 %


There will be regular homework assignments. The homeworks will include a mix of paper and pencil problems, and programming assignments. The assignments are due by 11:59pm on the day specified on the assignment. In general, no extensions will be granted.

Programming assignments. Please see the rules for submitting programming assignments.

Absences and late assignments. If an absence is unavoidable, you are still responsible for making arrangements to turn in the assignments on time. You are also responsible for obtaining any materials passed out and the information announced during the missed class. In case of extraordinary circumstances (hospitalization, family emergency) you should contact me as soon as possible so that we may arrange an extension for assignments prior to the due date. Documentation will be required. In all other cases, if an assignment can be accepted late, the penalty is 10% per day up to 5 days including Saturday, Sunday, and holidays. Assignments are due by 11:59pm on the due date. The timestamp on the dropbox or ftp submission will be used as well. There are NO makeup possibilities for exams.

Academic Integrity

All the work in this course except for presentation of papers should be done independently. Collaborations on homeworks are not permitted. Cheating and any other antiintellectual behavior, including giving your work to someone else, will be dealt with severely. If you feel you may have violated the rules speak to us as soon as possible.

Please make sure you read, understand and abide by the University's Academic Integrity Code . Students in this course will be expected to comply with the University of Pittsburgh's Policy on Academic Integrity. Any student suspected of violating this obligation for any reason during the semester will be required to participate in the procedural process, initiated at the instructor level, as outlined in the University Guidelines on Academic Integrity. This may include, but is not limited to, the confiscation of the examination of any individual suspected of violating University Policy. Furthermore, no student may bring any unauthorized materials to an exam, including dictionaries and programmable calculators.

Students With Disabilities

If you have a disability for which you are or may be requesting an accomodation, you are encouraged to contact both your instructor and Disability Resources and Services, 140 William Pitt Union, (412) 648-7890/(412) 383-7355 (TTY), as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course.


Notes include materials from Pitt Professors Hauskrecht, Hwa, and Wiebe, from the AIMA page, and from others on the web.