  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. The present work presents solutions
for plan generation when robustness of probability assessments must be
included: plans contain information about the robustness of certain actions.
The surprising result is that some problems 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 robustness 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.
