Počet záznamů: 1
Trust Estimation in Forecasting-Based Knowledge Fusion
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SYSNO ASEP 0549011 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV O - Ostatní Název Trust Estimation in Forecasting-Based Knowledge Fusion Tvůrce(i) Kárný, Miroslav (UTIA-B) RID, ORCID
Karlík, Daniel (UTIA-B)Celkový počet autorů 2 Zdroj.dok. Proceedings of BNAIC/BeneLearn 2021. - Luxembourg : University of Luxembourg, 2021 / Leiva Luis A. ; Pruski Cédric ; Markovich Réka ; Najjar Amro ; Schommer Cristoph Rozsah stran s. 363-378 Poč.str. 16 s. Forma vydání Online - E Akce Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning 2021 /33./ Datum konání 10.11.2021 - 12.11.2021 Místo konání Belval, Esch-sur-Alzette Země LU - Lucembursko Typ akce WRD Jazyk dok. eng - angličtina Země vyd. LU - Lucembursko Klíč. slova Trust ; Knowledge sharing ; Forecasting ; Fusion ; Decision making ; Bayesianism Vědní obor RIV BD - Teorie informace Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP LTC18075 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy Institucionální podpora UTIA-B - RVO:67985556 Anotace 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. Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2022
Počet záznamů: 1