PARIS: A Polynomial-Time, Risk-Sensitive Scheduling Algorithm for Probabilistic Simple Temporal Networks with Uncertainty - Santana, Pedro; Vaquero, Tiago; Toledo, Cláudio; Wang, Andrew; Fang, Cheng; Williams, Brian (Proceedings of the International Conference on Automated Planning and Scheduling, 2016)
Inspired by risk-sensitive, robust scheduling for planetary rovers under temporal uncertainty, this work introduces the Probabilistic Simple Temporal Network with Uncertainty (PSTNU), a temporal planning formalism that unifies the set-bounded and probabilistic temporal uncertainty models from the STNU and PSTN literature. By allowing any combination of these two types of uncertainty models, PSTNU's can more appropriately reflect the varying levels of knowledge that a mission operator might have regarding the stochastic duration models of different activities. We also introduce PARIS, a novel sound and provably polynomial-time algorithm for risk-sensitive strong scheduling of PSTNU's. Due to its fully linear problem encoding for typical temporal uncertainty models, PARIS is shown to outperform the current fastest algorithm for risk-sensitive strong PSTN scheduling by nearly four orders of magnitude in some instances of a popular probabilistic scheduling dataset, while results on a new PSTNU scheduling dataset indicate that PARIS is, indeed, amenable for deployment on resource-constrained hardware.