Researcher ORCID Identifier

Popowski, Lindsay 0000-0002-5649-0286

Akmal, Shyan 0000-0002-7266-2041

Li, Hemeng 0000-0002-9004-516X

Student Co-author

HMC Undergraduate

Document Type

Article

Department

Computer Science (HMC)

Publication Date

10-13-2020

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. © 2020 The Author(s). Published by Elsevier B.V.

Comments

Originally published in Artificial Intelligence, Vol. 289, by Elsevier ScienceDirect, 2020.

Rights Information

© 2020 The Author(s). Published by Elsevier B.V.

Terms of Use & License Information

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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