Abstract
| Original language | English |
|---|---|
| Title of host publication | Scalable Uncertainty Management |
| Subtitle of host publication | 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings |
| Editors | Davide Ciucci, Gabriella Pasi, Barbara Vantaggi |
| Place of Publication | Cham |
| Publisher | Springer International Publishing AG |
| Pages | 50-63 |
| Number of pages | 14 |
| ISBN (Print) | 978-3-030-00461-3 |
| DOIs | |
| Publication status | Published - 2018 |
| Event | International Conference on Scalable Uncertainty Management: Scalable Uncertainty Management - Milan, Italy Duration: 3 Oct 2018 → 5 Oct 2018 https://link.springer.com/book/10.1007/978-3-030-00461-3 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer |
| Volume | 11142 |
Conference
| Conference | International Conference on Scalable Uncertainty Management |
|---|---|
| Abbreviated title | SUM 2018 |
| Country | Italy |
| City | Milan |
| Period | 3/10/18 → 5/10/18 |
| Internet address |
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Explaining the Most Probable Explanation. / Butz, Raphaela; Hommersom, Arjen; van Eekelen, Marko.
Scalable Uncertainty Management: 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings. ed. / Davide Ciucci; Gabriella Pasi; Barbara Vantaggi. Cham : Springer International Publishing AG, 2018. p. 50-63 (Lecture Notes in Computer Science; Vol. 11142).Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review
TY - GEN
T1 - Explaining the Most Probable Explanation
AU - Butz, Raphaela
AU - Hommersom, Arjen
AU - van Eekelen, Marko
PY - 2018
Y1 - 2018
N2 - The use of Bayesian networks has been shown to be powerful for supporting decision making, for example in a medical context. A particularly useful inference task is the most probable explanation (MPE), which provides the most likely assignment to all the random variables that is consistent with the given evidence. A downside of this MPE solution is that it is static and not very informative for (medical) domain experts. In our research to overcome this problem, we were inspired by recent research results on augmenting Bayesian networks with argumentation theory. We use arguments to generate explanations of the MPE solution in natural language to make it more understandable for the domain expert. Moreover, the approach allows decision makers to further explore explanations of different scenarios providing more insight why certain alternative explanations are considered less probable than the MPE solution.
AB - The use of Bayesian networks has been shown to be powerful for supporting decision making, for example in a medical context. A particularly useful inference task is the most probable explanation (MPE), which provides the most likely assignment to all the random variables that is consistent with the given evidence. A downside of this MPE solution is that it is static and not very informative for (medical) domain experts. In our research to overcome this problem, we were inspired by recent research results on augmenting Bayesian networks with argumentation theory. We use arguments to generate explanations of the MPE solution in natural language to make it more understandable for the domain expert. Moreover, the approach allows decision makers to further explore explanations of different scenarios providing more insight why certain alternative explanations are considered less probable than the MPE solution.
U2 - 10.1007/978-3-030-00461-3_4
DO - 10.1007/978-3-030-00461-3_4
M3 - Conference article in proceeding
SN - 978-3-030-00461-3
T3 - Lecture Notes in Computer Science
SP - 50
EP - 63
BT - Scalable Uncertainty Management
A2 - Ciucci, Davide
A2 - Pasi, Gabriella
A2 - Vantaggi, Barbara
PB - Springer International Publishing AG
CY - Cham
ER -