Elsevier

Artificial Intelligence

Volume 289, December 2020, 103384
Artificial Intelligence

Quantifying controllability in temporal networks with uncertainty

https://doi.org/10.1016/j.artint.2020.103384Get rights and content
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Abstract

Controllability for Simple Temporal Networks with Uncertainty (STNUs) has thus far been limited to three levels: strong, dynamic, and weak. Because of this, there is currently no systematic way for an agent to assess just how far from being controllable an uncontrollable STNU is. We provide new insights inspired by a geometric interpretation of STNUs to introduce the degrees of strong and dynamic controllability — continuous metrics that measure how far a network is from being controllable. We utilize these metrics to approximate the probabilities that an STNU can be dispatched successfully offline and online respectively. We introduce new methods for predicting the degrees of strong and dynamic controllability for uncontrollable networks. We further generalize these metrics by defining likelihood of controllability, a controllability measure that applies to Probabilistic Simple Temporal Networks (PSTNs). Finally, we empirically demonstrate that these metrics are good predictors of actual dispatch success rate for STNUs and PSTNs.

Keywords

Scheduling
Temporal planning
Controllability
Probabilistic simple temporal networks
Simple temporal networks with uncertainty

Cited by (0)

This paper is an invited revision of a paper [1] which first appeared at the 2019 International Conference on Automated Planning and Scheduling (ICAPS-19).

1

Work on this paper was primarily completed while an undergraduate student at Harvey Mudd College.