August 1, 1996 – August 3, 1996

Uncertainty in Artificial Intelligence (UAI) '96

Location: Portland, Oregon, USA

Wednesday, July 31

Time	Session
8:00–8:30	Conference and Course Registration
8:35–5:30	Full-Day Course on Uncertain Reasoning

Thursday, August 1

Time	Session
8:00–8:40	Main Conference Registration
8:45–10:15	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
10:15–10:30	Break
10:30–12:00	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
12:00–12:45	Panel Discussion: Reports from the front: Real-world experiences with uncertain reasoning systems
12:45–2:00	Lunch
2:00–3:40	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
3:40–4:00	Poster Session I: Overview Presentations
4:00–6:00	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
7:30–9:30	UAI ’96 Meeting on Bayes Net Interchange Format

Friday, August 2

Time	Session
8:45–10:15	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)
10:15–10:30	Break
10:30–12:00	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
12:00–12:45	Panel Discussion: “Automated construction of models: Why, How, When?”
12:45–2:00	Lunch
2:00–3:30	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
3:40–4:00	Poster Session II: Overview Presentations
4:00–6:00	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
7:15–9:30	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
8:45–10:00	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
10:00–10:15	Break
10:15–11:45	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
11:45–12:30	Panel Discussion: “Learning and Uncertainty: The Next Steps”
12:30–2:00	Lunch
2:00–3:45	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
3:45–4:00	Break
4:00–5:00	Panel Discussion: “UAI by 2005: Reflections on critical problems, directions, and likely achievements for the next decade”
5:00–5:20	Report on the Bayes Net Interchange Format Meeting
5:30–6:00	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
8:30–12:00	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
9:45–10:05	Break
10:05–12:00	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