0549011 20240103230257.720211202235959.9 16 s. E cav_un_epca*0549010363-378LuxembourgUniversity of Luxembourg2021LeivaLuis A.PruskiCédricMarkovichRékaNajjarAmroSchommerCristoph Trust Knowledge sharing Forecasting Fusion Decision making Bayesianism cav_un_auth*0101124 Kárný Miroslav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0418093 Karlík Daniel UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2021/AS/karny-0549011.pdf CA 16228 COST (European Cooperation in Science and Technology) XE cav_un_auth*0389414 LTC18075 GA MŠk CZ cav_un_auth*0372050 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. cav_un_auth*0418094 Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning 2021 /33./ 20211110 20211112 Belval, Esch-sur-Alzette LU BD 10000 10200 10201 2022 2 PR RVO:67985556 http://hdl.handle.net/11104/0325137 S 2021 SCOPUS PUBMED WOS cav_un_epca*0549010 Proceedings of BNAIC/BeneLearn 2021 University of Luxembourg 2021 Luxembourg 363 378 Leiva Luis A. 340 Pruski Cédric 340 Markovich Réka 340 Najjar Amro 340 Schommer Cristoph 340