Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Integrated Uncertainty in Knowledge Modelling and Decision Making - 11th International Symposium, IUKM 2025, Proceedings |
| Herausgeber/-innen | Van-Nam Huynh, Katsuhiro Honda, Bac Le, Masahiro Inuiguchi, Hieu T. Huynh |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 85-97 |
| Seitenumfang | 13 |
| ISBN (elektronisch) | 978-981-96-4603-6 |
| ISBN (Print) | 9789819646029 |
| Publikationsstatus | Veröffentlicht - 24 März 2025 |
| Veranstaltung | 11th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2025 - Ho Chi Minh City, Vietnam Dauer: 17 März 2025 → 19 März 2025 |
Publikationsreihe
| Name | Lecture Notes in Computer Science |
|---|---|
| Band | 15586 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
For a complex engineering system – such as a city’s street network – it is important to predict how its functionality is decreased when some of these components break down, and, if repairs are needed and repairs budget is limited, which subset of the set of components should be repaired first to maximize the resulting functionality. For systems with a large number of components, the number of possible subsets is astronomical, we cannot try to simulate all these subsets. So, the natural idea is to approximate the actual dependence of functionality on the subset by a simple expression – linear or quadratic – and to use known algorithms for optimizing such approximate expressions. In this paper, we provide an algorithm for such an approximation, and we show that for linear approximations, the resulting expression is a generalization of Shapley value – a techniques that is now successfully use to make machine-learning-based AI explainable. We also analyze how the Shapley value idea can be further improved.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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- RIS
Integrated Uncertainty in Knowledge Modelling and Decision Making - 11th International Symposium, IUKM 2025, Proceedings. Hrsg. / Van-Nam Huynh; Katsuhiro Honda; Bac Le; Masahiro Inuiguchi; Hieu T. Huynh. Springer Science and Business Media Deutschland GmbH, 2025. S. 85-97 (Lecture Notes in Computer Science; Band 15586 LNAI).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - How Shapley Value and Its Generalizations Can Help in the Analysis of Complex Engineering Systems and What Next
AU - Winnewisser, Niklas
AU - Beer, Michael
AU - Kosheleva, Olga
AU - Kreinovich, Vladik
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025/3/24
Y1 - 2025/3/24
N2 - For a complex engineering system – such as a city’s street network – it is important to predict how its functionality is decreased when some of these components break down, and, if repairs are needed and repairs budget is limited, which subset of the set of components should be repaired first to maximize the resulting functionality. For systems with a large number of components, the number of possible subsets is astronomical, we cannot try to simulate all these subsets. So, the natural idea is to approximate the actual dependence of functionality on the subset by a simple expression – linear or quadratic – and to use known algorithms for optimizing such approximate expressions. In this paper, we provide an algorithm for such an approximation, and we show that for linear approximations, the resulting expression is a generalization of Shapley value – a techniques that is now successfully use to make machine-learning-based AI explainable. We also analyze how the Shapley value idea can be further improved.
AB - For a complex engineering system – such as a city’s street network – it is important to predict how its functionality is decreased when some of these components break down, and, if repairs are needed and repairs budget is limited, which subset of the set of components should be repaired first to maximize the resulting functionality. For systems with a large number of components, the number of possible subsets is astronomical, we cannot try to simulate all these subsets. So, the natural idea is to approximate the actual dependence of functionality on the subset by a simple expression – linear or quadratic – and to use known algorithms for optimizing such approximate expressions. In this paper, we provide an algorithm for such an approximation, and we show that for linear approximations, the resulting expression is a generalization of Shapley value – a techniques that is now successfully use to make machine-learning-based AI explainable. We also analyze how the Shapley value idea can be further improved.
KW - Complex engineering systems
KW - Shapley value
KW - Street network
UR - http://www.scopus.com/inward/record.url?scp=105002721515&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-4603-6_8
DO - 10.1007/978-981-96-4603-6_8
M3 - Conference contribution
AN - SCOPUS:105002721515
SN - 9789819646029
T3 - Lecture Notes in Computer Science
SP - 85
EP - 97
BT - Integrated Uncertainty in Knowledge Modelling and Decision Making - 11th International Symposium, IUKM 2025, Proceedings
A2 - Huynh, Van-Nam
A2 - Honda, Katsuhiro
A2 - Le, Bac
A2 - Inuiguchi, Masahiro
A2 - Huynh, Hieu T.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2025
Y2 - 17 March 2025 through 19 March 2025
ER -