An improved evidence theory-based trust model for multiagent resource allocation

Ningkui Wang (1) , Hayfa Zgaya-Biau (1) , Philippe Mathieu (1) , Slim Hammadi (1)

1 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189

In a resource allocation system, resource suppliers and customers can be naturally modelled as autonomous and interactive entities. In this context, we propose in this paper a multiagent system architecture based on trust and honesty concepts between agents in order to synchronize resource allocation in a distributed environment. Indeed, agents use several means in order to allocate resources efficiently as dialogue, adaptability, cooperation, collaboration and even negotiation in addition to the notion of trust. So, individual agents have to evaluate the trustworthiness of others to select those to interact with. Moreover , resource inadequacies can exist in the resource allocation systems, and it is difficult to meet the resource requirements of all agents simultaneously. To overcome these difficulties, we propose a distributed multiagent resource allocation system that emphasizes the issues of agents trust and resource inadequacies, which is called MARA-T&R. In this system, the evidence theory is improved thanks to the Deng entropy to estimate the trustwor-thiness of agents. Additionally, we use the concept of reservations to solve the problem of resource inadequacies. Our simulation results highlight the excellent performances of this improved trust model and the efficiency of the proposed MARA-T&R system for resource allocation.

Mots clés: Resource allocation; Trust; Uncertainty; Resource inadequacy; Multiagent system

Domaines: Informatique [cs]; Modélisation et simulation

Ningkui Wang, Hayfa Zgaya-Biau, Philippe Mathieu, Slim Hammadi. An improved evidence theory-based trust model for multiagent resource allocation. IEEE International Conference on Systems, Man, and Cybernetics (SMC-2020), Oct 2020, Toronto (Virtual Conference), Canada. pp.600-607, ⟨10.1109/SMC42975.2020.9283483⟩. ⟨hal-02941526⟩