| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0549011 |
| utime |
20240103230257.7 |
| mtime |
20211202235959.9 |
| title
(primary) (eng) |
Trust Estimation in Forecasting-Based Knowledge Fusion |
| specification |
| page_count |
16 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0549010 |
| title
|
Proceedings of BNAIC/BeneLearn 2021 |
| page_num |
363-378 |
| publisher |
| place |
Luxembourg |
| name |
University of Luxembourg |
| year |
2021 |
|
| editor |
| name1 |
Leiva |
| name2 |
Luis A. |
|
| editor |
| name1 |
Pruski |
| name2 |
Cédric |
|
| editor |
| name1 |
Markovich |
| name2 |
Réka |
|
| editor |
|
| editor |
| name1 |
Schommer |
| name2 |
Cristoph |
|
|
| keyword |
Trust |
| keyword |
Knowledge sharing |
| keyword |
Forecasting |
| keyword |
Fusion |
| keyword |
Decision making |
| keyword |
Bayesianism |
| author
(primary) |
| ARLID |
cav_un_auth*0101124 |
| name1 |
Kárný |
| name2 |
Miroslav |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0418093 |
| name1 |
Karlík |
| name2 |
Daniel |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
CA 16228 |
| agency |
COST (European Cooperation in Science and Technology) |
| country |
XE |
| ARLID |
cav_un_auth*0389414 |
|
| project |
| project_id |
LTC18075 |
| agency |
GA MŠk |
| country |
CZ |
| ARLID |
cav_un_auth*0372050 |
|
| abstract
(eng) |
Inference and decision making (DM) are ultimate goals of the artificialintelligence use. Complexity of DM tasks is the main barrier of their efficient solutions. Complex tasks are solved by dividing them among cooperating agents. This requires a knowledge fusion at a solution stage. It always has to cope with uncertainty. The used Bayesianism quantifies the uncertain knowledge by a probability density (pd) of modelled variables. The knowledge accumulation evolves the posterior pd of a parameter in the parametric model of observations. Bayes’rule updates the posterior pd. It provides a lossless compression of the knowledge in the observed data. An extended Bayes’ rule enables the use of knowledge coded in a forecaster of the modelled observations supplied by an agent’sneighbour. This rule exploits a weight expressing the trust into the forecaster. The paper offers yet-missing, algorithmic, data-based choice of this weight. It applies Bayesian estimation while assuming an invariant trust weight. Simulated examples illustrate behaviour of the resulting algorithm. They inspect its sensitivity to violation of the assumed credibility invariance. This prepares solutions coping with volatile knowledge sources. |
| action |
| ARLID |
cav_un_auth*0418094 |
| name |
Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning 2021 /33./ |
| dates |
20211110 |
| mrcbC20-s |
20211112 |
| place |
Belval, Esch-sur-Alzette |
| country |
LU |
|
| RIV |
BD |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2022 |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0325137 |
| confidential |
S |
| arlyear |
2021 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0549010 Proceedings of BNAIC/BeneLearn 2021 University of Luxembourg 2021 Luxembourg 363 378 |
| mrcbU67 |
Leiva Luis A. 340 |
| mrcbU67 |
Pruski Cédric 340 |
| mrcbU67 |
Markovich Réka 340 |
| mrcbU67 |
Najjar Amro 340 |
| mrcbU67 |
Schommer Cristoph 340 |
|