Bayesian and non-Bayesian evidential updating
Artificial Intelligence
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Two views of belief: belief as generalized probability and belief as evidence
Artificial Intelligence
Robotic Perception of Material: Experiments with Shape-Invariant Acoustic Measures of Material Type
The 4th International Symposium on Experimental Robotics IV
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Decision making with interval influence diagrams
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Partially observable Markov decision processes with imprecise parameters
Artificial Intelligence
Independence with lower and upper probabilities
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Propagation of 2-monotone lower probabilities on an undirected graph
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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Quasi-Bayesian theory uses convex sets of probability distributions and expected loss to represent preferences about plans. The theory focuses on decision robustness, i.e., the extent to which plans are affected by deviations in subjective assessments of probability. Generating a plan means enumerating the actions to be taken and providing information about the robustness of the actions. The present work presents plan generation problems that can be solved faster in the Quasi-Bayesian framework than within usual Bayesian theory. We investigate this on the planning to observe problem, i.e., an agent must decide whether to take new observations or not. The fundamental question is: How, and how much, to search for a "best" plan, based on the precision of probability assessments? Plan generation algorithms are derived in the context of material classification with an acoustic robotic probe. A package that constructs Quasi-Bayesian plans is available through anonymous ftp.