[an error occurred while processing this directive]

ICS-175A, Bayesian and Constraint Networks, Spring 2004
grades | home work | lecture notes

  • Classroom: ICS 180
  • Days: Monday & Wednesday
  • Time: 11:00 - 12:20pm
  • Instructor: Rina Dechter - dechter@ics.uci.edu
    Office: CS 424-E
    Hours: Wednesday, 10:00 am - 11:00 pm
  • TA: Anna Nash, nash@uci.edu
    Office: CS 180
    Hours: Monday, 11:00 am - 12:30 pm
    Hours: Friday, 11:00 am - 12:00
  • Reader: Guy Yosiphon, gyosipho@ics.uci.edu
    Office: CS/E 104
    Hours: Tuesday & Thursday, 11:00 am - 1:00 pm

Course Goals:
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 fill-up 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 Monday. we will have individual/group meetings and TA meetings each Wednesday.

Final grade: Weekly project reports, 20%.
Demo-presentation: 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) and constraint networks. Students can choose projects from other areas in AI, such as search and 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:
  1. 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.

  2. Some Ideas For Domains
  3. Requirements For the Bayesian Modeling Project
  4. 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:
  1. TBD

Resources on the Internet


Week Topic Date  
Week 1
  • Overview of necessary background in Bayes networks. Start forming groups for projects.
Week 2
  • Presentation of specific projects. Each group provides a proposal for two possible projects it considers.
Week 3
  • Progress report.
Week 4
  • Progress report.
Week 5
  • Progress report.
Week 6
  • Mid-quarter progress report and presentation.
Week 7
  • Progress report.
Week 8
  • End of eight week draft of final report.
Week 9
  • Demo-presentations.
Week 10
  • Demo-presentations.
Week 11
  • (Finals): final report + code + demo-presentation.