Robotics Institute, School of Computer Science, Carnegie Mellon University
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.
The original paper contains mathematical expressions that are not easily translated to HTML; the sections that use too many mathematics were greatly reduced and summarized. For a complete version of the paper, the compressed postscript version is indicated.
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Sun Jul 14 18:32:36 EDT 1996