Chance-Constrained Probabilistic Simple Temporal Problems


  • Cheng Fang MIT
  • Peng Yu MIT
  • Brian Williams MIT



chance-constrained, probabilistic


Scheduling under uncertainty is essential to many autonomous systems and logistics tasks. Probabilistic methods for solving temporal problems exist which quantify and attempt to minimize the probability of schedule failure. These methods are overly conservative, resulting in a loss in schedule utility. Chance constrained formalism address over-conservatism by imposing bounds on risk, while maximizing utility subject to these risk bounds. In this paper we present the probabilistic Simple Temporal Network (pSTN), a probabilistic formalism for representing temporal problems with bounded risk and a utility over event timing. We introduce a constrained optimisation algorithm for pSTNs that achieves compactness and efficiency through a problem encoding in terms of a parameterised STNU and its reformulation as a parameterised STN. We demonstrate through a car sharing application that our chance-constrained approach runs in the same time as the previous probabilistic approach, yields solutions with utility improvements of at least 5% over previous arts, while guaranteeing operation within the specified risk bound.




How to Cite

Fang, C., Yu, P., & Williams, B. (2014). Chance-Constrained Probabilistic Simple Temporal Problems. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).