Quasi-Bayesian Strategies for Efficient Plan Generation: Application to the Planning to Observe Problem

Fabio Cozman Eric Krotkov
fgcozman@cs.cmu.edu
Robotics Institute, School of Computer Science, Carnegie Mellon University
Pittsburgh, PA

Abstract:

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.

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These pages contain the main points of the original paper, presented at the Twelfth Conference on Uncertainty in Artificial Intelligence, August 1-3, 1996, Reed College Portland, Oregon, USA; the conference is organized by the Association for Uncertainty in Artificial Intelligence.

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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• INTRODUCTION
• THE QUASI-BAYESIAN FRAMEWORK
• BUILDING A NEW APPROACH TO DECISION-MAKING
• PLANNING TO OBSERVE WITH GAUSSIAN MEASUREMENTS
• CONCLUSION
• THE QUASI-BAYESIAN RISK
• A PACKAGE FOR QUASI-BAYESIAN PLAN GENERATION
• References