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Trust Estimation in Forecasting-Based Knowledge Fusion
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SYSNO ASEP 0549011 Document Type C - Proceedings Paper (int. conf.) R&D Document Type O - Ostatní Title Trust Estimation in Forecasting-Based Knowledge Fusion Author(s) Kárný, Miroslav (UTIA-B) RID, ORCID
Karlík, Daniel (UTIA-B)Number of authors 2 Source Title Proceedings of BNAIC/BeneLearn 2021. - Luxembourg : University of Luxembourg, 2021 / Leiva Luis A. ; Pruski Cédric ; Markovich Réka ; Najjar Amro ; Schommer Cristoph Pages s. 363-378 Number of pages 16 s. Publication form Online - E Action Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning 2021 /33./ Event date 10.11.2021 - 12.11.2021 VEvent location Belval, Esch-sur-Alzette Country LU - Luxembourg Event type WRD Language eng - English Country LU - Luxembourg Keywords Trust ; Knowledge sharing ; Forecasting ; Fusion ; Decision making ; Bayesianism Subject RIV BD - Theory of Information OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects LTC18075 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Institutional support UTIA-B - RVO:67985556 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2022
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