1. UAI 1985: Los Angeles, CA, USA

• Raj Bhatnagar, Laveen N. Kanal:
  Handling Uncertain Information: A Review of Numeric and Non-numeric Methods. 3-26
  
• Carlos Alberto Berenstein, Laveen N. Kanal:
  Consensus Rules. 27-34
  
• B. Chandrasekaran, Michael C. Tanner:
  Uncertainty Handling in Expert Systems: Uniform vs. Task-Specific Formalisms. 35-46
  
• David J. Spiegelhalter:
  Probabilistic Reasoning in Predictive Expert Systems. 47-68
  
• Ben P. Wise, Max Henrion:
  A Framework for Comparing Uncertain Inference Systems to Probability. 69-84
  
• Peter Cheeseman:
  Probabilistic vs. Fuzzy Reasoning. 85-102
  
• Lotfi A. Zadeh:
  Is Probability Theory Sufficient for Dealing with Uncertainty in AI: A Negative View. 103-116
  
• John F. Lemmer:
  Confidence Factors, Empiricism and the Dempster-Shafer Theory of Evidence. 117-126
  
• Glenn Shafer:
  Probability Judgment in Artificial Intelligence. 127-136
  
• Eric Horvitz, David Heckerman:
  The Inconsistent Use of Measures of Certainty in Artificial Intelligence Research. 137-152
  
• Benjamin N. Grosof:
  Evidential Confirmation as Transformed Probability: On the Duality of Priors and Updates. 153-166
  
• David Heckerman:
  Probabilistic Interpretation for MYCIN's Certainty Factors. 167-196
  
• Rodney W. Johnson:
  Independence and Bayesian Updating Methods. 197-202
  
• Daniel Hunter:
  Uncertain Reasoning Using Maximum Entropy Inference. 203-210
  
• John E. Shore:
  Relative Entropy, Probabilistic Inference, and AI. 211-216
  
• Piero P. Bonissone, Keith Decker:
  Selecting Uncertainty Calculi and Granularity: An Experiment in Trading-off Precision and Complexity. 217-248
  
• Alf C. Zimmer:
  What Uncertainty Judgments Can Tell About the Underlying Subjective Probabilities. 249-258
  
• Benjamin N. Grosof:
  An Inequality Paradigm for Probabilistic Knowledge: The Logic of Conditional Probability Intervals. 259-278
  
• Marvin S. Cohen:
  An Expert System Framework for Non-monotonic Reasoning About Probabilistic Assumptions. 279-294
  
• Robert M. Fung, Chee Yee Chong:
  Metaprobability and Dempster-Shafer in Evidential Reasoning. 295-302
  
• Gerald Liu:
  Knowledge Structures and Evidential Reasoning in Decision Analysis. 303-316
  
• Ronald R. Yager:
  A General Approach to Decision Making with Evidential Knowledge. 317-330
  
• Matthew L. Ginsberg:
  Implementing Probabilistic Reasoning. 331-338
  
• Samuel Holtzman, Jack S. Breese:
  Exact Reasoning About Uncertainty: On the Design of Expert Systems for Decision Support. 339-346
  
• Tod S. Levitt:
  Model-Based Probabilistic Situation Inference in Hierarchical Hypothesis Spaces. 347-356
  
• Judea Pearl:
  A Constraint-Propagation Approach to Probabilistic Reasoning. 357-370
  
• Ross D. Shachter:
  Intelligent Probabilistic Inference. 371-382
  
• David S. Vaughan, Bruce M. Perrin, Robert M. Yadrick, Peter D. Holden, Karl G. Kempf:
  An Odds Ratio Based Inference Engine. 383-392
  
• Norman C. Dalkey:
  Inductive Inference and the Representation of Uncertainty. 393-398
  
• Henry Hamburger:
  Representing, Combining and Using Uncertain Estimates. 399-414
  
• Stephen Jose Hanson, Malcolm Bauer:
  Machine Learning, Clustering and Polymorphy. 415-428
  
• Larry A. Rendell:
  Induction, of and by Probability. 429-446
  
• John Fox:
  Three Arguments for Extending the Framework of Probability. 447-458
  
• Ronald Prescott Loui:
  Interval-Based Decisions for Reasoning Systems. 459-472
  
• Ray J. Solomonoff:
  The Application of Algorithmic Probability to Problems in Artificial Intelligence. 473-494
  
• Bruce Abramson:
  An Explanation of and Cure for Minimax Pathology. 495-504
  
• Dana S. Nau, Paul Walton Purdom Jr., Chun-Hung Tzeng:
  An Evaluation of Two Alternatives to Minimax. 505-509
  

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