Trust Estimation in Forecasting-Based Knowledge Fusion

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00549011" target="_blank" >RIV/67985556:_____/21:00549011 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Trust Estimation in Forecasting-Based Knowledge Fusion

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Trust Estimation in Forecasting-Based Knowledge Fusion

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LTC18075" target="_blank" >LTC18075: Distribuované racionální rozhodování: kooperační aspekty</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů