Quasi-Bayesian Strategies for Efficient Plan Generation: Application to the Planning to Observe Problem
This work addresses the challenge of decision robustness in planning for agents needing to balance observation costs with plan quality, offering incremental improvements in efficiency for domain-specific applications.
The paper tackles the problem of generating robust plans under uncertain probability assessments in the planning to observe problem, showing that the Quasi-Bayesian framework can solve some problems faster than traditional Bayesian methods, with specific applications in material classification using an acoustic robotic probe.
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.