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Privacy in distributed constrained problems for utility-based agents

Abstract : Although the field of multi-agent systems has been largely studied, interactions between agents imply privacy loss. Indeed, solving distributed problems, being frequently combinatorial, implies an extensive exchange of information between agents until an agreement is found. The problem is that existing approaches do not generally consider privacy and focus only on the satisfaction of agents’ constraints to evaluate solution. The works presented in this thesis therefore aim at considering systematically the issue of privacy in distributed reasoning. We show that existing works in the field still let agents preserve implicitly some degree of privacy. We propose an approach based on utility theory, a formal setting well defined in Artificial Intelligence, allowing an objective and quantitative approach to the interests and reasonable behaviours of agents. More precisely, the model we have developed includes non only the usual parameters but also information on agents privacy quantified in term of utility. We also show that these problems must be considered as planning problems where agents choose actions maximizing their utility. Common algorithms can be described as plans usable as generic models by intelligent planners. Conducted experiments let us validate the approach and evaluate the quality of obtained solution, while showing that their efficiency can be improved thanks to privacy considerations.
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Submitted on : Tuesday, October 5, 2021 - 2:10:16 PM
Last modification on : Tuesday, October 19, 2021 - 11:38:41 AM

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  • HAL Id : tel-03365887, version 1

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Julien Savaux. Privacy in distributed constrained problems for utility-based agents. Computer Science [cs]. Université de Valenciennes et du Hainaut-Cambrésis, 2017. English. ⟨NNT : 2017VALE0030⟩. ⟨tel-03365887⟩

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