2. UAI 1986: Philadelphia, PA, USA

• Norman C. Dalkey:
  Models vs inductive inference for dealing with probabilistic knowledge. 3-10
  
• David Heckerman:
  An axiomatic framework for belief updates. 11-22
  
• David Heckerman, Eric Horvitz:
  The myth of modularity in rule-based systems for reasoning with uncertainty. 23-34
  
• Steven J. Henkind:
  Imprecise meanings as a cause of uncertainty in medical knowledge-based systems. 35-42
  
• Robert A. Hummel, Michael S. Landy:
  Evidence as opinions of experts. 43-54
  
• Mary McLeish:
  Probabilistic logic: some comments and possible use for nonmonotonic reasoning. 55-62
  
• Richard M. Tong, Lee A. Appelbaum:
  Experiments with interval-valued uncertainty. 63-76
  
• Robert M. Yadrick, Bruce M. Perrin, David S. Vaughan, Peter D. Holden, Karl G. Kempf:
  Evaulation of uncertain inference models I: PROSPECTOR. 77-88
  
• Ben P. Wise:
  Experimentally comparing uncertain inference systems to probability. 89-102
  
• Stephen W. Barth, Steven W. Norton:
  Knowledge engineering within a generalised bayesian framework. 103-114
  
• Kaihu Chen:
  Learning to predict: an inductive approach. 115-124
  
• Brian Falkenhainer:
  Towards a general-purpose belief maintenance system. 125-132
  
• Sally A. Goldman, Ronald L. Rivest:
  A non-iterative maximum entropy algorithm. 133-148
  
• Max Henrion:
  Propagating uncertainty in bayesian networks by probabilistic logic sampling. 149-164
  
• Steven W. Norton:
  An explanation mechanism for bayesian inferencing systems. 165-174
  
• Shimon Schocken:
  On the rational scope of probabilistic rule-based inference systems. 175-190
  
• Ross D. Shachter:
  DAVID: influence diagram processing system for the macintosh. 191-196
  
• Michael P. Wellman:
  Qualitativce probabilistic networks for planning under uncertainty. 197-208
  
• Ronald R. Yager:
  On implementing usual values. 209-220
  
• Su-Shing Chen:
  Some extensions of probabilistic logic. 221-228
  
• A. Julian Craddock, Roger A. Browse:
  Belief as summarization and meta-support. 229-236
  
• Benjamin N. Grosof:
  Non-monotonicity in probabilistic reasoning. 237-250
  
• James Hawthorne:
  A semantic approach to non-monotonic entailments. 251-262
  
• Henry E. Kyburg Jr.:
  Knowledge. 263-272
  
• Ronald Prescott Loui:
  Computing reference classes. 273-290
  
• Judea Pearl:
  Distributedrevision of belief commitment in composite explanations. 291-316
  
• Ross D. Shachter, David Heckerman:
  A backwards view for assessment. 317-324
  
• Prakash P. Shenoy, Glenn Shafer, Khaled Mellouli:
  Propagation of belief functions: a distributed approach. 325-336
  
• Silvio Ursic:
  Generalising fuzzy logic probabilistic inferences. 337-364
  
• Shoshana Abel:
  The sum-and-lattice-points method based on an evidential-reasoning system applied to the real-time vehicle guidance problem. 365-370
  
• Lashon B. Booker, Naveen Hota:
  Probabilistic reasoning about ship images. 371-380
  
• Gregory D. Hager, Hugh F. Durrant-Whyte:
  Information and multi-sensor coordination. 381-394
  
• B. R. Fox, Karl G. Kempf:
  Planning, scheduling and uncertainty in the sequence of future events. 395-402
  
• Ze-Nian Li, Leonard Uhr:
  Evidential reasoning in a computer vision system. 403-412
  
• Tod S. Levitt:
  Bayesian inference for radar imagery based surveillance. 413-422
  
• Stanley M. Schwartz, Jonathan Baron, John R. Clarke:
  A causal bayesian model for the diagnosis of appendicitis. 423-434
  
• Randall Smith, Matthew Self, Peter Cheeseman:
  Estimating uncertain spatial relationships in robotics. 435-461
  

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