Approximately Optimal Monitoring of Plan Preconditions
This addresses the challenge of costly monitoring in planning systems, though it appears incremental as it builds on existing POMDP frameworks.
The paper tackles the problem of efficiently monitoring plan preconditions to enable replanning when failures occur, by formulating it as a partially-observable Markov decision process and showing that single-precondition monitoring is tractable, with multiple-precondition policies approximated using single-precondition solutions.
Monitoring plan preconditions can allow for replanning when a precondition fails, generally far in advance of the point in the plan where the precondition is relevant. However, monitoring is generally costly, and some precondition failures have a very small impact on plan quality. We formulate a model for optimal precondition monitoring, using partially-observable Markov decisions processes, and describe methods for solving this model efficitively, though approximately. Specifically, we show that the single-precondition monitoring problem is generally tractable, and the multiple-precondition monitoring policies can be efficitively approximated using single-precondition soultions.