 Classroom: IERF B015
 Days: Tuesday & Thursday
 Time: 12:30  1:50pm
 Instructor: Rina Dechter  dechter@ics.uci.edu
Office: CS 424E
Hours: Thursday, 11:00 am  12:00 pm.
 TA: Robert Mateescu
Office: CS/E 332
Hours: Monday, 11:00 am  12:30 pm.
 Reader: Radu Marinescu, radum@ics.uci.edu
Office : Office: CS/E 331
Hours: Monday, 11:00 am  1:00 pm.
Course Goals:
Requirements
Students are required to do a project in Artificial Intelligence. Weekly progress reports will be graded.
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 fillup the necessary gaps in their background. TA and instructor will help with an introductory overview and refer the students to the appropriate literature. In particular, basic knowledge in Bayesain networks and Constraint processing will be necessary.
We will have a class meeting each week on Tuesday. we will have individual/group meetings and TA meetings each Thursday.
Grading:
Final grade: Weekly project reports, 20%.
Demopresentation: 30%
Final report: 50%.
Projects Ideas:
There will be two types of projects. Project of building an AI system that provide advise in some area. We will use graphical model frameworks and focus primarily on Bayesian Networks (BN). Students can choose projects from other areas in AI, such as search and constraint satisfaction, and planning. The second type is "Research projects". Students will delve into a research question with a graduate student and will conduct empirical investigation pursuing the question at stake.
Students can select a proposed prject or may also come up with a proposal of their own which is relevant to an AI class.
System Building Projects:
 Primary focus of the lab:
The project is to build a Bayesian network that models a domain and makes some inferences. Available tools such as REES, Hugin and JavaBayes can be used. The system can be built using knowledge acquisition from expert in the domain or by learning from data or both. Following are domains used in the past.
Admission to a Phd program.
Loan expert advisor
Basketball simulator
Handicappers (piking winners in horseracing)
Selfpreserving building
Choose your domain, model it and deonstrate query processing over your model by REES/JavaBayes/Hugin.
 Some Ideas For Domains
 Requirements For the Bayesian Modeling Project
 Systems based on constraint networks
TA assignments: Given class schedules for quarter, the number of TA needed for each class, TA’s preferences and qualification, and instructors’ choice of preferred TA, schedule the TA’s in a way that maximize some measure of staisfaction. (Real data may be available)(Talk to Andre Deloach)
Class scheduling:
The problem is to find a schedule for classes, classrooms, and teachers for a teaching seting (e.g., a high school, a computer science department). Measure of satisfaction can be to minimize the number of weighted constraints violated. Students can use the REES tool to model and run their algorithms.
Combinatorial auctions:
Research with a graduate student:
 Triangulation algorithms, inducedwidth, cyclecustet,wcutste.
Many algorithms applied to graphical models (Bayesian networks and constraintnetworks) have complexity related to a graph parameter known as inducedwidth. The task will be to implement a variety of approximation methods (greedy methods, local search methods) for inducedwidth and compare on real benchmarks and randomly generated networks.
The problem of findinging minimum inducedwidth is related to graphs' preprocessing for triangulation. The problem is to find atriangulation of a graph such that the maximum size of its cliques is minimal. In a recent paper several rules are used to preprocess the initial graph in orderto reduce it to a smaller graph before triangulating it. The triangulationof the original graph can then be obtained by reversing the reductionsteps.
Another graph related investigations are to find a cyclesutest of a graph. Yet another related problem is to find a wcutset of a graph.
References
 Hans L. Bodlaender et al., "Preprocessing for Triangulation of Probabilistic Networks", Proceedings of UAI, 2001
 Judea Pearl. "Probabilistic Reasoning in Intelligent Systems". Morgan Kaufman, ch. 3.2.4 (graph triangulation algorithm)
 A. Becker, R. BarYehuda, D. Geiger, "Random Algorithms for the Loop Cutset Problem", UAI, 1999
 A. Becker, D. Geiger, "Approximation Algorithms for the Loop Cutset Problem", UAI 1994
Most of these papers can be retrieved from http://citeseer.nj.nec.com.
 Experimenting with Iterative belief propagation.
 Bozhena's Debuging domain.
Resources on the Internet
 Books
 Survey Papers
 Survey on Constraint Processing
 Dechter, R., "Constraint Networks (Survey)". In Encyclopedia of Artificial Intelligence, 2nd edition, 1992, John Wiley & Sons, Inc., pp. 276285.
 Dechter, R., Rossi, F., "Constraint Satisfaction". Survey ECS, March, 2000.
 Artificial Intelligence: A Modern Approach, Chapter 5: Constraint Satisfaction Problems
 ICS270A: Lecture 5
 ICS275A course webpage.
 Tutorials
 Tools
 More Links
Schedule:
Week 
Topic 
Date 

Week 1 
 Overview of necessary background in Bayes networks.
Start forming groups for projects.

0401 

Week 2 
 Presentation of specific projects. Each group provides a proposal for two possible projects it considers.

0408 

Week 3 

0415 

Week 4 

0422 

Week 5 

0429 

Week 6 
 Midquarter progress report and presentation.

0506 

Week 7 

0513 

Week 8 
 End of eight week draft of final report.

0520 

Week 9 

0527 

Week 10 

0605 

Week 11 
 (Finals): final report + code + demopresentation.

0612 
