UAI-95 - 11th Conference on Uncertainty in AI McGill University, Montreal, Quebec, 18-20 August 1995 =================================== Final Program =================================== ============================== Friday 18 August Overview ============================== 08:45 -- 09:00 Opening remarks 09:00 -- 10:15 Invited talk #1 (Haussler) 10:15 -- 10:30 Break 10:30 -- 12:30 Presentation session #1 12:30 -- 14:00 Lunch 14:00 -- 16:00 Poster session #1 16:00 -- 16:15 Break 16:30 -- 18:30 Presentation session #2 ================================ Saturday 19 August Overview ================================ 09:00 -- 10:30 Invited talk #2 (Jordan) + panel discussion 10:30 -- 10:45 Break 10:45 -- 12:45 Presentation session #3 12:45 -- 14:30 Lunch 14:30 -- 16:00 Invited talk #3 (Subrahmanian) 16:00 -- 16:15 Break 16:15 -- 18:15 Presentation session #4 ================================ Sunday 20 August Overview ================================ 09:00 -- 10:30 Invited talk #4 (Shafer) + panel discussion 10:30 -- 10:45 Break 10:45 -- 12:45 Presentation session #5 12:45 -- 14:30 Lunch 14:30 -- 16:00 Poster session #2 16:00 -- 16:15 Break 16:15 -- 18:15 Presentation session #6 ============================================== Invited talks ============================================== #1 Haussler "Hidden Markov and Related Statistical Models: How They Have Been Applied to Biosequence Analysis" #2 Jordan (with panel on learning) "A Few Relevant Ideas from Statistics, Neural Networks, and Statistical Mechanics" #3 Subrahamanian "Uncertainty in Deductive Databases" #4 Shafer (with panel on causality) "The Multiple Causal Interpretation of Bayes Nets" ================================================= Presentation session #1 ================================================= Wellman/Ford/Larson PATH PLANNING UNDER TIME-DEPENDENT UNCERTAINTY Horvitz/Barry DISPLAY OF INFORMATION FOR TIME-CRITICAL DECISION MAKING Pearl/Robins PROBABILISTIC EVALUATION OF SEQUENTIAL PLANS FROM CAUSAL MODELS WITH HIDDEN VARIABLES Haddawy/Doan/Goodwin EFFICIENT DECISION-THEORETIC PLANNING: TECHNIQUES AND EMPIRICAL ANALYSIS Fargier/Lang/Clouaire/Schiex A CONSTRAINT SATISFACTION FRAMEWORK FOR DECISION UNDER UNCERTAINTY ================================================= Presentation session #2 ================================================= Xu/Smets GENERATING EXPLANATIONS FOR EVIDENTIAL REASONING ========> Best student paper <=========== Meek CAUSAL INFERENCE AND CAUSAL EXPLANATION WITH BACKGROUND KNOWLEDGE ========> Best student paper <=========== Cayrac/Dubois/Prade PRACTICAL MODEL-BASED DIAGNOSIS WITH QUALITATIVE POSSIBILISTIC UNCERTAINTY Srinivas/Horvitz EXPLOITING SYSTEM HIERARCHY TO COMPUTE REPAIR PLANS IN PROBABILISTIC MODEL-BASED DIAGNOSIS Balke/Pearl COUNTERFACTUALS AND POLICY ANALYSIS IN STRUCTURAL MODELS ================================================= Presentation session #3 ================================================= Jensen CAUTIOUS PROPAGATION IN BAYESIAN NETWORKS Darwiche STRONG CONDITIONING ALGORITHMS FOR EXACT AND APPROXIMATE INFERENCE IN CAUSAL NETWORKS ========> Best student paper <=========== Draper CLUSTERING WITHOUT (THINKING ABOUT) TRIANGULATION ========> Best student paper <=========== Goldszmidt FAST BELIEF UPDATE USING ORDER-OF-MAGNITUDE PROBABILITIES ========> Best student paper <=========== Harmanec TOWARD A CHARACTERIZATION OF UNCERTAINTY MEASURE FOR THE DEMPSTER-SHAFER THEORY ========> Best student paper <=========== ================================================= Presentation session #4 ================================================= Dubois/Prade NUMERICAL REPRESENTATION OF ACCEPTANCE Grosof TRANSFORMING PRIORITIZED DEFAULTS AND SPECIFICITY INTO PARALLEL DEFAULTS Weydert DEFAULTS AND INFINITESIMALS DEFEASIBLE INFERENCE BY NONARCHIMEDEAN ENTROPY-MAXIMIZATION Benferhat/Saffiotti/Smets BELIEF FUNCTIONS AND DEFAULT REASONING Ngo/Haddawy/Helwig A THEORETICAL FRAMEWORK FOR CONTEXT-SENSITIVE TEMPORAL PROBABILITY MODEL CONSTRUCTION WITH APPLICATION TO PLAN PROJECTION ========================================== Presentation session #5 ========================================== Campos/Moral INDEPENDENCE CONCEPTS FOR CONVEX SETS OF PROBABILITIES Geiger/Heckerman A CHARACTERIZATION OF THE DIRICHLET DISTRIBUTION THROUGH GLOBAL AND LOCAL INDEPENDENCE Spirtes DIRECTED CYCLIC GRAPHICAL REPRESENTATIONS OF FEEDBACK MODELS Pynadath/Wellman ACCOUNTING FOR CONTEXT IN PLAN RECOGNITION, WITH APPLICATION TO TRAFFIC MONITORING Srinivas MODELING FAILURE PRIORS AND PERSISTENCE IN MODEL-BASED DIAGNOSIS ========================================== Presentation session #6 ========================================== Poole EXPLOITING THE RULE STRUCTURE FOR DECISION MAKING WITHIN THE INDEPENDENT CHOICE LOGIC Krause/Fox/Judson IS THERE A ROLE FOR QUALITATIVE RISK ASSESSMENT? Srinivas POLYNOMIAL ALGORITHM FOR COMPUTING THE OPTIMAL REPAIR STRATEGY IN A SYSTEM WITH INDEPENDENT COMPONENT FAILURES Boldrin/Sossai AN ALGEBRAIC SEMANTICS FOR POSSIBILISTIC LOGIC Hajek/Godo/Esteva FUZZY LOGIC AND PROBABILITY ============================================================== Poster session #1 ============================================================== 1. Jack Breese, Russ Blake. AUTOMATING COMPUTER BOTTLENECK DETECTION WITH BELIEF NETS 2. Wray L. Buntine CHAIN GRAPHS FOR LEARNING 3. J.L. Castro, J.M. Zurita AN APPROACH TO GET THE STRUCTURE OF A FUZZY RULE UNDER UNCERTAINTY 4. Tom Chavez, Ross Shachter DECISION FLEXIBILITY 5. Arthur L. Delcher, Adam Grove, Simon Kasif, Judea Pearl LOGARITHMIC-TIME UPDATES AND QUERIES IN PROBABILISTIC NETWORKS 6. Eric Driver, Darryl Morrell CONTINUOUS BAYESIAN NETWORKS 7. Nir Friedman, Joseph Y. Halpern PLAUSIBILITY MEASURES: A USER'S GUIDE 8. David Galles, Judea Pearl TESTING IDENTIFIABILITY OF CAUSAL EFFECTS 9. Steve Hanks, David Madigan, Jonathan Gavrin PROBABILISTIC TEMPORAL REASONING WITH ENDOGENOUS CHANGE 10. David Heckerman BAYESIAN METHODS FOR LEARNING CAUSAL NETWORKS 11. Eric Horvitz, Adrian Klein STUDIES IN FLEXIBLE LOGICAL INFERENCE: A DECISION-MAKING PERSPECTIVE 12. George John, Pat Langley ESTIMATING CONTINUOUS DISTRIBUTIONS IN BAYESIAN CLASSIFIERS 13. Uffe Kjaerulff HUGS: COMBINING EXACT INFERENCE AND GIBBS SAMPLING IN JUNCTION TREES 14. Prakash P. Shenoy A NEW PRUNING METHOD FOR SOLVING DECISION TREES AND GAME TREES 15. Peter Spirtes, Christopher Meek, Thomas Richardson CAUSAL INFERENCE IN THE PRESENCE OF LATENT VARIABLES AND SELECTION BIAS 16. Nic Wilson AN ORDER OF MAGNITUDE CALCULUS 17. S.K.M. Wong, C.J. Butz, Y. Xiang A METHOD FOR IMPLEMENTING A PROBABILISTIC MODEL AS A RELATIONAL DATABASE
18. Y. Xiang OPTIMIZATION OF INTER-SUBNET BELIEF UPDATING IN MULTIPLY SECTIONED BAYESIAN NETWORKS 19. Nevin Lianwen Zhang INFERENCE WITH CAUSAL INDEPENDENCE IN THE CPSC NETWORK =============================================== Poster Session #2 =============================================== 1. Fahiem Bacchus, Adam Grove GRAPHICAL MODELS FOR PREFERENCE AND UTILITY 2. Enrique Castillo, Remco R. Bouckaert, Jose M. Sarabia, Cristina Solares ERROR ESTIMATION IN APPROXIMATE BAYESIAN BELIEF NETWORK INFERENCE 3. David Maxwell Chickering A NEW CHARACTERIZATION OF EQUIVALENT BAYESIAN NETWORK STRUCTURES 4. Marek J. Druzdzel, Linda C. van der Gaag ELICITATION OF PROBABILITIES: COMBINING QUALITATIVE AND QUANTITATIVE INFORMATION 5. Kazuo J. Ezawa, Til Schuermann LEARNING SYSTEM: A RARE BINARY OUTCOME WITH MIXED DATA STRUCTURES 6. David Heckerman, Dan Geiger LEARNING BAYESIAN NETWORKS: A UNIFICATION FOR DISCRETE AND GAUSSIAN DOMAINS 7. David Heckerman, Ross Shachter A DEFINITION AND GRAPHICAL REPRESENTATION FOR CAUSALITY 8. Mark Hulme IMPROVED SAMPLING FOR DIAGNOSTIC REASONING IN BAYESIAN NETWORK 9. Ali Jenzarli INFORMATION/RELEVANCE INFLUENCE DIAGRAMS 10. Keiji Kanazawa, Daphne Koller, Stuart Russell STOCHASTIC SIMULATION ALGORITHMS FOR DYNAMIC PROBABILISTIC NETWORKS 11. Grigoris I. Karakoulas PROBABILISTIC EXPLORATION IN PLANNING WHILE LEARNING 12. Alexander V. Kozlov, Jaswinder Pal Singh APPROXIMATE PROBABILISTIC INFERENCE IN BELIEF NETWORKS 13. Michael L. Littman, Thomas L. Dean, Leslie Pack Kaelbling ON THE COMPLEXITY OF SOLVING MARKOV DECISION PROBLEMS 14. Chris Meek STRONG-COMPLETENESS AND FAITHFULNESS IN BAYES NETWORKS 15. Simon Parsons REFINING REASONING IN QUALITATIVE PROBABILISTIC NETWORKS 16. Judea Pearl ON THE TESTABILITY OF CAUSAL MODELS WITH LATENT AND INSTRUMENTAL VARIABLES 17. Gregory Provan ABSTRACTION IN BELIEF NETWORKS: THE ROLE OF INTERMEDIATE STATES IN DIAGNOSTIC REASONING 18. Marco Valtorta, Young-Gyun Kim ON THE DETECTION OF CONFLICTS IN DIAGNOSTIC BAYESIAN NETWORKS USING ABSTRACTION