Complexity Bounds for the Controllability of Temporal Networks with Conditions, Disjunctions, and Uncertainty
This provides foundational insights for temporal planning researchers dealing with complex models, though it is incremental in refining existing bounds.
The paper tackled the problem of determining tight computational complexity bounds for controllability checking in temporal networks with conditions, disjunctions, and uncertainty, establishing that all these problems are computable in PSPACE.
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 paper, 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.