Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Foundations of Intelligent Systems |
| Untertitel | 25th International Symposium, ISMIS 2020, Proceedings |
| Herausgeber/-innen | Denis Helic, Martin Stettinger, Alexander Felfernig, Gerhard Leitner, Zbigniew W. Ras |
| Erscheinungsort | Cham |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 56-65 |
| Seitenumfang | 10 |
| ISBN (elektronisch) | 978-3-030-59491-6 |
| ISBN (Print) | 9783030594909 |
| Publikationsstatus | Veröffentlicht - 17 Sept. 2020 |
| Veranstaltung | 25th International Symposium on Methodologies for Intelligent Systems, ISMIS 2020 - Graz, Österreich Dauer: 23 Sept. 2020 → 25 Sept. 2020 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 12117 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
The goal of decision making is to select the most suitable option from a number of possible alternatives. Which is easy, if all possible alternatives are known and evaluated. This case is rarely encountered in practice; especially in product development, decisions often have to be made under uncertainty. As uncertainty cannot be avoided or eliminated, actions have to be taken to deal with it. In this paper a tool from the field of artificial intelligence, decision networks, is used. Decision networks utilize probabilistic reasoning to model uncertainties with probabilities. If the influence of uncertainty cannot be avoided, a variation of the product is necessary so that it adjusts optimally to the changed situation. In contrast, robust products are insensitive to the influence of uncertainties. An application example from the engineering design has shown, that a conclusion about the robustness of a product for possible scenarios can be made by the usage of the decision network. It turned out that decision networks can support the designer well in making decisions under uncertainty.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Foundations of Intelligent Systems: 25th International Symposium, ISMIS 2020, Proceedings. Hrsg. / Denis Helic; Martin Stettinger; Alexander Felfernig; Gerhard Leitner; Zbigniew W. Ras. Cham: Springer Science and Business Media Deutschland GmbH, 2020. S. 56-65 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12117 LNAI).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Decision-Making with Probabilistic Reasoning in Engineering Design
AU - Plappert, Stefan
AU - Gembarski, Paul Christoph
AU - Lachmayer, Roland
PY - 2020/9/17
Y1 - 2020/9/17
N2 - The goal of decision making is to select the most suitable option from a number of possible alternatives. Which is easy, if all possible alternatives are known and evaluated. This case is rarely encountered in practice; especially in product development, decisions often have to be made under uncertainty. As uncertainty cannot be avoided or eliminated, actions have to be taken to deal with it. In this paper a tool from the field of artificial intelligence, decision networks, is used. Decision networks utilize probabilistic reasoning to model uncertainties with probabilities. If the influence of uncertainty cannot be avoided, a variation of the product is necessary so that it adjusts optimally to the changed situation. In contrast, robust products are insensitive to the influence of uncertainties. An application example from the engineering design has shown, that a conclusion about the robustness of a product for possible scenarios can be made by the usage of the decision network. It turned out that decision networks can support the designer well in making decisions under uncertainty.
AB - The goal of decision making is to select the most suitable option from a number of possible alternatives. Which is easy, if all possible alternatives are known and evaluated. This case is rarely encountered in practice; especially in product development, decisions often have to be made under uncertainty. As uncertainty cannot be avoided or eliminated, actions have to be taken to deal with it. In this paper a tool from the field of artificial intelligence, decision networks, is used. Decision networks utilize probabilistic reasoning to model uncertainties with probabilities. If the influence of uncertainty cannot be avoided, a variation of the product is necessary so that it adjusts optimally to the changed situation. In contrast, robust products are insensitive to the influence of uncertainties. An application example from the engineering design has shown, that a conclusion about the robustness of a product for possible scenarios can be made by the usage of the decision network. It turned out that decision networks can support the designer well in making decisions under uncertainty.
KW - Bayesian network
KW - Decision network
KW - Decision-making
KW - Engineering design
KW - Probabilistic reasoning
UR - http://www.scopus.com/inward/record.url?scp=85092104386&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59491-6_6
DO - 10.1007/978-3-030-59491-6_6
M3 - Conference contribution
AN - SCOPUS:85092104386
SN - 9783030594909
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 56
EP - 65
BT - Foundations of Intelligent Systems
A2 - Helic, Denis
A2 - Stettinger, Martin
A2 - Felfernig, Alexander
A2 - Leitner, Gerhard
A2 - Ras, Zbigniew W.
PB - Springer Science and Business Media Deutschland GmbH
CY - Cham
T2 - 25th International Symposium on Methodologies for Intelligent Systems, ISMIS 2020
Y2 - 23 September 2020 through 25 September 2020
ER -