Techniques for Generating Optimal, Robust Plans when Temporal Uncertainty is Present

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 21

AAAI / SIGART Doctoral Consortium

Planning under uncertainty has been well studied, but usually the uncertainty is in action outcomes. This work instead investigates uncertainty in the amount of time that actions require to execute. In addition to this temporal uncertainty, the problems being studied must have robust solution plans that are optimized based on an objective function. This thesis summary details two iterative approaches that have been used to solve these type of problems and discusses future work, including MDP approaches.