Beyond trust. Using fuzzy contextual filters for reliability assessment in multi agent systems

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Trust modeling is widely recognized as an aspect of essential importance in the construction of agents and multi agent systems (MAS). As a consequence, several trust formalisms have been proposed over the last years. All of them have a limitation: they can determine the trustworthiness or untrustworthiness of the information received from a given agent, but they don't supply mechanisms for correcting this information, in the case of it being unaccurate, in order to extract some utility from it. In order to overcome this limitation, this thesis introduces the concept of reliability as a generalization of trust, and presents Fuzzy Contextual Filters (FCF) as reliability modeling methods loosely based on system identification and signal processing techniques. This thesis illustrates their applicability to two domains: the appraisal variance estimation problem in the Agent Reputation and Trust (ART) testbed and Bar Systems, a class of optimization algorithms for reactive MAS ​
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