Cyber risks in networked autonomous systems
Abstract/Contents
- Abstract
- Operation of autonomous unmanned vehicles introduces new risks about which decisionmakers have neither exhaustive statistics nor similar systems from which to derive priors. This model-based risk analysis combines algorithms used for autonomous control, Monte Carlo simulations, and learning parameters from data to improve the risk model's performance. The results inform high-level decisionmakers on when and how best to employ autonomous unmanned vehicles in security and military applications where risk tolerance is higher than for civilian applications, while explicitly maintaining high-risk decisions as the responsibility of human decisionmakers, even when software is used in the process of executing those decisions. This risk analysis has applications in use of unmanned vehicles for localization of radio-frequency threats and maritime tracking of non-cooperative targets using linear array sonar.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Goldfrank, Joseph Abraham |
|---|---|
| Degree supervisor | Paté-Cornell, M. Elisabeth (Marie Elisabeth) |
| Thesis advisor | Paté-Cornell, M. Elisabeth (Marie Elisabeth) |
| Thesis advisor | Shachter, Ross D |
| Thesis advisor | Weyant, John P. (John Peter) |
| Degree committee member | Shachter, Ross D |
| Degree committee member | Weyant, John P. (John Peter) |
| Associated with | Stanford University, Department of Management Science and Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Joseph Goldfrank. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/bm167jp8407 |
Access conditions
- Copyright
- © 2022 by Joseph Abraham Goldfrank
- License
- This work is licensed under a Creative Commons Attribution Non Commercial No Derivatives 3.0 Unported license (CC BY-NC-ND).
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