ICS-275B Fall 2000, Network-Based Reasoning - Belief Networks |

Course Reference | homeworks & projects | handouts | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

- Days: Tuesday/Thursday
- Time: 2:00 p.m. - 3:20 p.m.
- Room: CS 213
- Instructor: Rina Dechter
Course Description
One of the main challenges in building intelligent systems is the ability
to reason under uncertainty, and one of the most successful approaches
for dealing with this challenge is based on the framework of Bayesian belief
networks. Intelligent systems based on Bayesian networks are currently
being used in a number of real-world applications including diagnosis,
sensor fusion,
on-line help systems, credit assessment, and data mining.
The objective of this class is to provide an in-depth exposition of knowledge representation and reasoning under uncertainty using the framework of belief networks. Both theoretical underpinnings and practical considerations will be covered, with a special emphasis on constructing graphical models and on exact and approximate inference algorithms. Additional topics include learning belief network parameters from Data, dynamic belief networks, reasoning about actions and planning under uncertainty.
Prerequisites
- Familiarity with basic concepts of probability theory.
- Knowledge of basic computer science, algorithms and programming principles.
- Previous exposure to AI is desirable but not essential.
Tentative Syllabus
Readings (partial list)
- Dechter, R.,
*"Bucket Elimination: A unifying framework for Reasoning."* - Judea Pearl,
*Probabilistic Reasoning in Intelligent Systems.*Heckerman & Breese,*Causal Independence for Probability Assessment and Inference Using Bayesian Networks*. - Boutilier, Friedman, Goldszmidt & Koller,
*Context-Specific Independence in Bayesian Networks*. - Dechter,
*Bucket Elimination: A Unifying Framework for Probabilistic Inference*. - Dechter, "AAAI98 tutorial on reasoning."
- Heckerman,
*A Tutorial on Learning with Bayesian Networks*. - Kjaerulff,
*dHugin: A Computational System for Dynamic Time-Sliced Bayesian Networks*. - Pearl,
*Causation, Action and Counterfactuals*. - Dechter,
*Mini-buckets: a general scheme for approximating inference.* - Darwiche,
*Recursive Conditioning: Any-space conditioning algorithm with treewidth-bounded complexity.* - Darwiche,
*Any-space probabilistic inference.* - Darwiche,
*On the role of partial differentiation in probabilistic inference.* - Horvitz, Breese, Heckerman, Hovel & Rommelse.
*The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users.* - Binder, Murphy, Russell.
*Space-efficient inference in dynamic probabilistic networks.* - Russell, Binder, Koller, Kanazawa.
*Local learning in probabilistic networks with hidden variables.* - Dugad & Desai.
*A Tutorial on Hidden Markov Models.* - Friedman, Geiger, Goldszmidt.
*Bayesian Network Classifiers.* - Dechter, R., El Fattah, Y.,
*Topological Parameters For Time-Space Tradeoff* - Gagliardi, F.,
*Generalizing Variable Elimination In Bayesian Networks* - Rish, I; Dechter, R,
*AAAI 2000 Tutorial*
Books:
- Judea Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, 1990.
- Finn V. Jensen. An introduction to Bayesian networks. UCL Press, 1996.
- Robert G. Cowell, A. Philip Dawid, Steffen L. Lauritzen, David J. Spiegelhalter Probabilistic Networks and Expert Systems Springer-Verlag, 1999
- Castillo, E.; Gutierrez, J.M.; Hadi, A.S., Expert Systems and Probabilistic Network Models, Springer-Verlag 1997
Free Software
- GeNIe/SMILE from the University of UPitt:
http://www2.sis.pitt.edu/~genie/ - Hugin lite from Hugin:
http://www.hugin.com - MSBN from Microsoft Research:
http://www.research.microsoft.com/dtas/msbn/ - JAVABayes from CMU:
http://www.cs.cmu.edu/~javabayes/Home/ - Netica from Norsys:
http://www.norsys.com
Related Links
- A Brief Introduction to Graphical Models and Bayesian Networks
- The Association for Uncertainty in Artificial Intelligence
Sponsors the conference on Uncertainty in Artificial Intelligence (UAI), which is the main yearly forum for reporting research results relating to Bayesian networks. -
FreeBayesian Network Packages
A Brief Introduction to Graphical Models and Bayesian Networks - Artificial Intelligence: Probability studies may give computers a chance to learn, by Kevin J. Delaney in Cambridge, England. The Wall Street Journal Europe.
- AI on the web
- UAI repository
- More Inference Algorithms
Assignments:
There will be homework assignments and students will also be engaged in
projects.
Grading Policy:
Homeworks and projects (50%), midterm (50%) |