# Complexity Bounds for the Controllability of Temporal Networks with Conditions, Disjunctions, and Uncertainty

arXiv: https://arxiv.org/abs/1901.02307 · HTML: https://mlsanity.com/paper/1901.02307/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2019.

## Built on by (most cited first, 3 of 3)

- [Time-based Dynamic Controllability of Disjunctive Temporal Networks with Uncertainty: A Tree Search Approach with Graph Neural Network Guidance](https://mlsanity.com/paper/2108.01068/) (2021)
- [Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability using Tree Search and Graph Neural Networks](https://mlsanity.com/paper/2203.15030/) (2022)
- [Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature](https://mlsanity.com/paper/2207.13181/) (2022)

## Abstract (arXiv)

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.