Complexity Bounds for the Controllability of Temporal Networks with Conditions, Disjunctions, and Uncertainty (Extended Abstract)
Complexity Bounds for the Controllability of Temporal Networks with Conditions, Disjunctions, and Uncertainty (Extended Abstract)
Nikhil Bhargava, Brian C. Williams
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Journal track. Pages 6353-6357.
https://doi.org/10.24963/ijcai.2019/886
In temporal planning, many different temporal network formalisms are used to model real world situations. Each of these formalisms has different features which affect how easy it is to determine whether the underlying network of temporal constraints is consistent. While many of the simpler models have been well-studied from a computational complexity perspective, the algorithms developed for advanced models which combine features have very loose complexity bounds. In this work, we provide tight completeness bounds for strong, weak, and dynamic controllability checking of temporal networks that have conditions, disjunctions, and temporal uncertainty. Our work exposes some of the subtle differences between these different structures and, remarkably, establishes a guarantee that all of these problems are computable in PSPACE.
Keywords:
Planning and Scheduling: Scheduling
Planning and Scheduling: Temporal and Hybrid planning
Planning and Scheduling: Planning under Uncertainty