Dr. Rina Dechter @ UCI

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CompSci-175, Project in AI, Spring 2011

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Classroom: ICS 180
Days: Tuesday & Thursday
Time: 2:00pm - 3:20pm
Instructor: Rina Dechter - dechter@ics.uci.edu
Office: DBH 4232
Hours: TBA
TA: William Lam - willmlam@ics.uci.edu
Office: DBH 4099
Hours: TBA

Requirements:

Students are required to do a project in Artificial Intelligence.

Final project: submit a report + code + demo + presentation.

Students will be required to work independently and be expected to acquire all the knowledge necessary for the project. They will have to fill in the necessary gaps in their background.

Grading:

Midterm report: 30%
Demo-presentation: 35%
Final report: 35%

Specific ideas for projects: see the projects page

Discussion Board

Assignments and progress reports

For any assignment that you submit you should always state at the top of the report: the team number, the names of team members and the project title.

How does the project build upon what is available in the literature and out there?
Important: What is the architecture of your project (different modules and who is doing what on the team)?
What AI algorithms will you be using?
what tools will you use, or have started to use?
Is there any data that you need to access?
How would you evaluate your project? What is the criteria for success?
What initial code have you started to develop?

Past Projects

Resources on the Internet

Books
- Constraint Processing by Rina Dechter, Morgan Kaufmann 2003
- Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig. Chapter 14, 15a, 15b
- Probabilistic Reasoning in Intelligent Systems, by Judea Pearl, Morgan Kaufmann, 1990.
- Modeling and Reasoning with Bayesian Networks by Adnan Darwiche, Campbridge University Press, 2009
Surveys on Constraint Processing
- Artificial Intelligence: A Modern Approach, Chapter 5: Constraint Satisfaction Problems
- Dechter, R., "Constraint Networks (Survey)". In Encyclopedia of Artificial Intelligence, 2nd edition, 1992, John Wiley & Sons, Inc., pp. 276-285.
- Dechter, R., Rossi, F., "Constraint Satisfaction". Survey ECS, March, 2000.
- ICS-270A: Lecture 5
Survey Papers on Bayesian Networks
- Introduction to Bayesian Networks: Practical and Technical Perspectives by Stefan Conrady and Lionel Jouffe
- Bayesian Networks by Adnan Darwiche
  CACM, December 2010.
- Bayesian Networks Without Tears, by Eugene Charniak
  AI Magazine, vol. 12, 1991.
- Graphical models in a nutshell
- A Brief Introduction to Graphical Models and Bayesian Networks by Kevin Murphy
- Bayesian Networks, by Judea Pearl and Stuart Russell
Tutorials
- AAAI-98 Tutorial: Learning Bayesian Networks From Data
Tools
- Numberjack (constraint programming platform for Python):
  http://4c110.ucc.ie/numberjack/
- SamIam by Darwiche (see books section for the companion textbook) :
  http://reasoning.cs.ucla.edu/samiam/
- REES
- JAVABayes from CMU:
  http://www.cs.cmu.edu/~javabayes/Home/
- Hugin lite from Hugin:
  http://www.hugin.com
- GeNIe/SMILE from the University of UPitt:
  http://genie.sis.pitt.edu/
- MSBNx from Microsoft Research:
  http://research.microsoft.com/en-us/um/redmond/groups/adapt/msbnx/
- BNT (Bayes Net Toolbox for Matlab):
  http://bnt.googlecode.com/
- Netica from Norsys:
  http://www.norsys.com
- BayesiaLab:
  http://www.conradyscience.com
Related project classes at other universities
- University of Pennsylvania
- Carnegie Mellon

Schedule:

Week	Topic	Date	Information	Assignment

Week 1	Overview of constraint networks	03-29	Slides

Week 2	Numberjack tutorial	04-05	Slides	Project Proposals

Week 3		04-12

Week 4	Overview of Bayesian networks	04-19	Slides

Week 5	Paper presentations	04-26		Draft progress report (due Tuesday)

Week 6	Midterm reports and presentations	05-03

Week 7		05-10

Week 8		05-17

Week 9		05-24

Week 10	Demo-presentations	05-31

Week 11	(Finals): final report + code + demo-presentation	06-09