UAI ’96 Full-Day Course on Uncertain Reasoning


Time Session

Introduction and Goals
Eric Horvitz and Finn Jensen


Session I: Foundations of Uncertainty

  • Foundations of Probability and Utility
    Instructor: Ross Shachter
  • Beyond probability: Alternative Formalisms
    Instructor: Prakash Shenoy
  • Review and Questions
    Shachter and Shenoy

Session II: Inference Algorithms for Belief and Action

  • Algorithms for probabilistic inference
    Instructor: Bruce D’Ambrosio
  • Decision making
    Instructor: Mark Peot
  • Commonalities in inference methods for uncertain reasoning
    Instructor: Finn Jensen
  • Review and Questions
    D’ambrosio, Jensen, and Peot


Session III: Modeling and Knowledge Acquisition
Instructors: Kathryn Laskey and Michael Shwe

Session IV: Learning Models from Data

  • Foundations of Learning Graphical Models
    Instructors: Greg Cooper, David Heckerman
  • Real-world Application of Learning Methods
    Instructor: Wray Buntine

Session V: Uncertain Reasoning in the Real World–Case Studies
Instructors: Eric Horvitz and Mark Peot


Research Directions / UAI 96 Highlights


Session I: Foundations of Uncertainty

Instructors: Ross Shachter and Prakash Shenoy

In the Foundations session, Ross Shachter and Prakash Shenoy introduced the basic principles of reasoning under uncertainty. The first part of Foundations included a presentation of important historical background, foundations of probability and decision making, and an introduction to the representation of uncertain knowledge with Bayesian networks and influence diagrams. In the second part of Foundations, Prakash Shenoy moved beyond probability theory to present alternative formalisms for reasoning under uncertainty. His discussion covered Dempster-Shafer belief functions, possibility theory, and work on abstraction of probability theory, including Spohn’s perspective on belief.

Session II: Inference Algorithms for Belief and Action

Instructors: Bruce D’Ambrosio, Mark Peot, and Finn Jensen

In the Inference Algorithms session, Bruce D’Ambrosio reviewed the basic principles of probabilistic inference algorithms with Bayesian networks. He covered the family of algorithms developed for inference and discussed their behaviors and applicability. Mark Peot discussed techniques for computing optimal policies in influence diagrams. Finally, Finn Jensen examined commonalities among inference algorithms in probabilistic and nonprobabilistic reasoning frameworks.

Session III: Modeling and Knowledge Acquisition

Instructors: Kathryn Laskey and Michael Shwe

Kathy Laskey and Michael Shwe reviewed problems and with the structuring and assessment of Bayesian networks and influence diagrams. Real-time knowledge acquisition was planned for this session so the audience could experience firsthand some of the real world issues involved with building models for reasoning under uncertainty.

Session IV: Learning Models from Data

Instructors: Greg Cooper, David Heckerman, and Wray Buntine

Wray Buntine, Greg Cooper, and David Heckerman introduced the fast growing area of learning graphical models from data. First, Greg Cooper and David Heckerman presented the foundations of learning graphical models, taking a causal perspective on influences among variables. They reviewed scores and search methods for model selection, including techniques from Bayesian statistics, neural-network research, and machine learning. After the presentation of basics, Wray Buntine described key factors to consider in the real-world application of the learning methods.

Session V: Uncertain Reasoning in the Real World—Case Studies

Instructors: Eric Horvitz and Mark Peot

Several case studies were presented that highlight multiple issues with the construction and fielding of real-world systems that rely on reasoning under uncertainty.