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Execution Monitoring to Improve Plans with Information Gathering

There has been much recent interest in planning problems with deterministic actions but stochastic observations. Examples include Mars rover planning, robot monitoring tasks and the Rocksample domain from the planning competition. However, theoretical results show that in general these problems are as hard as solving partially observable Markov decision problems (POMDPs). We propose an approach where we build plans assuming the observations are reliable, then monitor the execution of the plan and use a value of information calculation to add information gathering actions online. We describe two variants: one using a classical contingency planner to generate the initial plan, and the other using a Markov decision problem planner. We show how in both cases the addition of execution monitoring can considerably improve overall performance with lower computational cost than solving the original POMDP.