Uncertainty in Artificial Intelligence (UAI) ’96


uai96The UAI conference is organized under the auspices of the Association for Uncertainty in AI (AUAI). The Association home page contains information on several issues, including the UAI mailing list for email postings and discussions of topics related to the representation and management of uncertain information. UAI ’96 was the Twelfth Conference on Uncertainty in Artificial Intelligence.

UAI ’96 Full-Day Course

A one-day intensive UAI course was given on Wednesday, July 31, the day before the start of the main UAI ’96 conference. The course provided an immersive review of key topics in computational methods for reasoning under uncertainty. Access the Full-Day Course Program.

Program Chairs

General Conference Chair

  • Steve Hanks, University of Washington

Program Committee

  • Fahiem Bacchus, University of Waterloo
  • Salem Benferhat, IRIT Universite Paul Sabatier
  • Philippe Besnard, IRISA
  • Mark Boddy, Honeywell Technology Center
  • Piero Bonissone, General Electric Research Laboratoyy
  • Craig Boutilier, University of British Columbia
  • Jack Breese, Microsoft Research
  • Wray Buntine, Thinkbank
  • Luis M. de Campos, Universidad de Granada
  • Enrique Castillo, Universidad de Cantabria
  • Eugene Charniak, Brown University
  • Greg Cooper, University of Pittsburgh
  • Bruce D’Ambrosio, Oregon State University
  • Paul Dagum, Stanford University
  • Adnan Darwiche, Rockwell Science Center, Thousand Oaks
  • Tom Dean, Brown University
  • Denise Draper, University of Washington
  • Marek Druzdzel, University of Pittsburgh
  • Didier Dubois, IRIT Universite Paul Sabatier
  • Ward Edwards, University of Southern California
  • Kazuo Ezawa, AT&T Labs
  • Nir Friedman, Stanford University
  • Robert Fung, Prevision
  • Linda van der Gaag, Utrecht University
  • Hector Geffner, Universidad Simon Bolivar
  • Dan Geiger, Technion
  • Lluis Godo, Campus Universitat Autonoma Barcelona
  • Robert Goldman, Honeywell Technology Center
  • Moises Goldszmidt, Rockwell Palo Alto Laboratory
  • Adam Grove, NEC Research Institute
  • Peter Haddawy, University of Wisconsin-Milwaukee
  • Petr Hajek, Academy of Sciences, Czech Republic
  • Joseph Halpern, IBM Almaden Research Center
  • Steve Hanks, University of Washington
  • Othar Hansson, Thinkbank
  • Peter Hart, Ricoh California Research Center
  • David Heckerman, Microsoft Research
  • Max Henrion, Lumina
  • Frank Jensen, Hugin Expert A/S
  • Michael Jordan, MIT

  • Leslie Pack Kaelbling, Brown University
  • Uffe Kjaerulff, Aalborg University
  • Daphne Koller, Stanford University
  • Paul Krause, Imperial Cancer Research Fund
  • Rudolf Kruse, University of Braunschweig
  • Henry Kyburg, University of Rochester
  • Jerome Lang, IRIT Universite Paul Sabatier
  • Kathryn Laskey, George Mason University
  • Paul Lehner, George Mason University
  • John Lemmer, Rome Laboratory
  • Tod Levitt, IET
  • Ramon Lopez de Mantaras, Spanish Scientific Research Council, CSIC
  • David Madigan, University of Washington
  • Christopher Meek, Carnegie Mellon University
  • Serafin Moral, Universidad de Granada
  • Eric Neufeld, University of Saskatchewan
  • Ann Nicholson, Monash University
  • Ramesh Patil, Information Sciences Institute, USC
  • Judea Pearl, University of California, Los Angeles
  • Kim Leng Poh, National University of Singapore
  • David Poole, University of British Columbia
  • Henri Prade, IRIT Universite Paul Sabatier
  • Greg Provan, Institute for Learning Systems
  • Enrique Ruspini, SRI International
  • Romano Scozzafava, Dip. Me.Mo.Mat., Rome, Italy
  • Ross Shachter, Stanford University
  • Prakash Shenoy, University of Kansas
  • Philippe Smets, IRIDIA Universite libre de Bruxelles
  • David Spiegelhalter, Cambridge University
  • Peter Spirtes, Carnegie Mellon University
  • Milan Studeny, Academy of Sciences, Czech Republic
  • Sampath Srinivas, Microsoft
  • Jaap Suermondt, Hewlett Packard Laboratories
  • Marco Valtorta, University of South Carolina
  • Michael Wellman, University of Michigan
  • Nic Wilson, Oxford Brookes University
  • Y. Xiang, University of Regina
  • Hong Xu, IRIDIA Universite libre de Bruxelles
  • John Yen, Texas A&M University
  • Lian Wen Zhang, Hong Kong University of Science & Technology


Wednesday, July 31

Time Session
Conference and Course Registration

Thursday, August 1

Time Session
Main Conference Registration
Plenary Session I: Perspectives on Inference
  • Toward a Market Model for Bayesian Inference
    D. Pennock and M. Wellman
  • A unifying framework for several probabilistic inference algorithms
    R. Dechter
  • Computing upper and lower bounds on likelihoods in intractable networks
    T. Jaakkola and M. Jordan (Outstanding Student Paper Award)
  • Query DAGs: A practical paradigm for implementing belief-network inference
    A. Darwiche and G. Provan
Plenary Session II: Applications of Uncertain Reasoning
  • MIDAS: An Influence Diagram for Management of Mildew in Winter Wheat
    A. Jensen and F. Jensen
  • Optimal Factory Scheduling under Uncertainty using Stochastic Dominance A*
    P. Wurman and M. Wellman
  • Supply Restoration in Power Distribution Systems — A Case Study in Integrating Model-Based Diagnosis and Repair Planning
    S. Thiebaux, M. Cordier, O. Jehl, J. Krivine
  • Network Engineering for Complex Belief Networks
    S. Mahoney and K. Laskey
Panel Discussion: Reports from the front:
Real-world experiences with uncertain reasoning systems


Plenary Session III: Representation and Independence
  • Context-Specific Independence in Bayesian Networks
    C. Boutilier, N. Friedman, M. Goldszmidt, D. Koller
  • Binary Join Trees
    P. Shenoy
  • Why is diagnosis using belief networks insensitive to imprecision in probabilities?
    M. Henrion, M. Pradhan, K. Huang, B. del Favero, G. Provan, P. O’Rorke
  • On separation criterion and recovery algorithm for chain graphs
    Milan Studeny
Poster Session I: Overview Presentations
Poster Session I
  • Inference Using Message Propagation and Topology Transformation in Vector Gaussian Continuous Networks
    S. Alag and A. Agogino
  • Constraining Influence Diagram Structure by Generative Planning: An Application to the Optimization of Oil Spill Response
    J. Agosta
  • An Alternative Markov Property for Chain Graphs
    S. Andersson, D. Madigan, and M. Perlman
  • Object Recognition with Imperfect Perception and Redundant Description
    C. Barrouil and J. Lemaire
  • A Sufficiently Fast Algorithm for Finding Close to Optimal Junction Trees
    A. Becker and D. Geiger
  • Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network
    D. Chickering and D. Heckerman
  • Independence with Lower and Upper Probabilities
    L. Chrisman
  • Topological Parameters for Time-Space Tradeoff
    R. Dechter
  • A Qualitative Markov Assumption and its Implications for Belief Change
    N. Friedman and J. Halpern
  • A Probabilistic Model for Sensor Validation
    P. Ibarguengoytia and L. Sucar
  • Bayesian Learning of Loglinear Models for Neural Connectivity
    K. Laskey and L. Martignon
  • Geometric Implications of the Naive Bayes Assumption
    M. Peot
  • Optimal Monte Carlo Estimation of Belief Network Inference
    M. Pradhan and P. Dagum
  • A Discovery Algorithm for Directed Cyclic Graphs
    Thomas Richardson
  • Real-Time Estimation of Bayesian Networks
    R. Welch
  • Testing Implication of Probabilistic Dependencies
    S.K.M. Wong
UAI ’96 Meeting on Bayes Net Interchange Format

Friday, August 2

Time Session

Plenary Session IV: Time, Persistence, and Causality

  • A Structurally and Temporally Extended Bayesian Belief Network Model: Definitions, Properties, and Modelling Techniques
    C. Aliferis and G. Cooper
  • Identifying independencies in causal graphs with feedback
    J. Pearl and R. Dechter
  • Topics in Decision-Theoretic Troubleshooting: Repair and Experiment
    J. Breese and D. Heckerman
  • A Polynomial-Time Algorithm for Deciding Equivalence of Directed Cyclic Graphical Models
    T. Richardson (Outstanding Student Paper Award)



Plenary Session V: Planning and Action under Uncertainty

  • A Measure of Decision Flexibility
    R. Shachter and M. Mandelbaum
  • A Graph-Theoretic Analysis of Information Value
    K. Poh and E. Horvitz
  • Sound Abstraction of Probabilistic Actions in The Constraint Mass Assignment Framework
    A. Doan and P. Haddawy
  • Flexible Policy Construction by Information Refinement
    M. Horsch and D. Poole

Panel Discussion: “Automated construction of models: Why, How, When?”




Plenary Session VI: Qualitative Reasoning and Abstraction of Probability

  • Generalized Qualitative Probability
    D. Lehmann
  • Uncertain Inferences and Uncertain Conclusions
    H. Kyburg, Jr.
  • Arguing for Decisions: A Qualitative Model of Decision Making
    B. Bonet and H. Geffner
  • Defining Relative Likelihood in Partially Ordered Preferential Structures
    J. Halpern

Poster Session II: Overview Presentations


Poster Session II

  • An Algorithm for Finding Minimum d-Separating Sets in Belief Networks
    S. Acid and L. de Campos
  • Plan Development using Local Probabilistic Models
    E. Atkins, E. Durfee, K. Shin
  • Entailment in Probability of Thresholded Generalizations
    D. Bamber
  • Coping with the Limitations of Rational Inference in the Framework of Possibility Theory
    S. Benferhat, D. Dubois, H. Prade
  • Decision-Analytic Approaches to Operational Decision Making: Application and Observation
    T. Chavez
  • Learning Equivalence Classes of Bayesian Network Structures
    D. Chickering
  • Propagation of 2-Monotone Lower Probabilities on an Undirected Graph
    L. Chrisman
  • Quasi-Bayesian Strategies for Efficient Plan Generation: Application to the Planning to Observe Problem
    F. Cozman and E. Krotkov
  • Some Experiments with Real-Time Decision Algorithms
    B. D’Ambrosio and S. Burgess
  • An Evaluation of Structural Parameters for Probabilistic Reasoning: Results on Benchmark Circuits
    Y. El Fattah and R. Dechter
  • Learning Bayesian Networks with Local Structure
    N. Friedman M. Goldszmidt
  • Theoretical Foundations for Abstraction-Based Probabilistic Planning
    V. Ha and P. Haddawy
  • Probabilistic Disjunctive Logic Programming
    L. Ngo
  • A Framework for Decision-Theoretic Planning I: Combining the Situation Calculus, Conditional Plans, Probability and Utility
    D. Poole
  • Coherent Knowledge Processing at Maximum Entropy by SPIRIT
    W. Roedder and C. Meyer
  • Efficient Enumeration of Instantiations in Bayesian Networks
    S. Srinivas and P. Nayak

UAI ’96 Reception and Invited Talk

Failing and Succeeding at Real-World Reasoning under Uncertainty: Reflections on Three Decades of Work
Peter Hart

Saturday, August 3

Time Session

Plenary Session VII: Developments in Belief and Possibility

  • Belief Revision in the Possibilistic Setting with Uncertain Inputs
    D. Dubois and H. Prade
  • Approximations for Decision Making in the Dempster-Shafer Theory of Evidence
    M. Bauer
  • Possible World Partition Sequences: A Unifying Framework for Uncertain Reasoning
    C. Teng



Plenary Session VIII: Learning and Uncertainty

  • Asymptotic model selection for directed networks with hidden variables
    D. Geiger, D. Heckerman, C. Meek
  • On the Sample Complexity of Learning Bayesian Networks
    N. Friedman and Z. Yakhini
  • Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates
    C. Boutilier
  • Critical Remarks on Single Link Search in Learning Belief Networks
    Y. Xiang, S.K.M Wong, N. Cercone

Panel Discussion: “Learning and Uncertainty: The Next Steps”




Plenary Session IX: Advances in Approximate Inference

  • Computational complexity reduction for BN2O networks using similarity of states
    A. Kozlov and J. Singh
  • Sample-and-Accumulate Algorithms for Belief Updating in Bayes Networks
    E. Santos Jr., S. Shimony, E. Williams
  • Tail Simulation in Bayesian Networks
    E. Castillo, C. Solares, P. Gomez
  • Efficient Search-Based Inference for Noisy-OR Belief Networks: TopEpsilon
    K. Huang and M. Henrion



Panel Discussion: “UAI by 2005: Reflections on critical problems, directions, and likely achievements for the next decade”


Report on the Bayes Net Interchange Format Meeting


UAI Planning Meeting

Sunday, August 4

UAI-KDD Special Joint Sessions
Oregon Convention Center

Selected talks on learning graphical models from the UAI and KDD proceedings.
UAI badges will be honored at the Oregon Convention Center for the joint session.

Time Session

Plenary Session X: Learning, Probability, and Graphical Models I

  • KDD: Knowledge Discovery and Data Mining: Toward a Unifying Framework
    U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth
  • UAI: Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network
    D. Chickering and D. Heckerman
  • KDD: Clustering using Monte Carlo Cross-Validation
    P. Smyth
  • UAI: Learning Equivalence Classes of Bayesian Network Structures
    D. Chickering



Plenary Session XI: Learning, Probability, and Graphical Models II

  • UAI: Learning Bayesian Networks with Local Structure
    N. Friedman M. Goldszmidt
  • KDD: Rethinking the Learning of Belief Network Probabilities
    R. Musick
  • UAI: Bayesian Learning of Loglinear Models for Neural Connectivity
    K. Laskey and L. Martignon
  • KDD: Harnessing Graphical Structure in Markov Chain Monte Carlo Learning
    P. Stolorz