An Approach on Merging Agents’ Trust Distributions in a Possibilitic Domain - Agents and Artificial Intelligence - page 278

An Approach on Merging Agents' Trust Distributions
in a Possibilitic Domain

Sina Honari 1 , Brigitte Jaumard 1 , and Jamal Bentahar 2

1 Department of Computer Science and Software Engineering, Concordia University,
1455 De Maisonneuve Blvd. West, Montreal, Canada
2 Concordia Institute for Information System Engineering, Concordia University,
1515 Ste-Catherine Street West, Montreal, Canada
s [e-mail], [e-mail],
[e-mail]

Abstract. In this paper, we propose a novel approach on merging the trust dis-
tributions of an explorer agent in its advisors with the trust distributions of the
advisor agents in a target agent. These two sets of merged distributions represent
the trust of different, yet connected, agents in a multi-agent system. The deduced
distribution measures an approximation of the explorer agent's trust in the tar-
get agent. The proposed approach can serve as a building block for estimating
the trust distribution of an agent of interest in the multi-agent systems, who is
accessible indirectly through a set of sequentially connected agents.
A common issue of modelling trust is the presence of uncertainty, which arises
in scenarios where there is either lack of adequate information or variability in
an agent's level of trustworthiness. In order to represent uncertainty, possibility
distributions are used to model trust of the agents.

Keywords: Possibility theory, Uncertainty, Trust, Multi-agent systems.

1 Introduction

Social networking sites have become the preferred venue for social interactions. Despite
the fact that social networks are ubiquitous on the Internet, only few websites exploit
the potential of combining user communities and online marketplaces. The reason is
that users do not know which other users to trust, which makes them suspicious of
engaging in online business, in particular if many unknown other parties are involved.
This situation, however, can be alleviated by developing trust metrics such that a user
can assess and identify trustworthy users. In the present study, we focus on developing
a trust metric for estimating the trust of a target agent, who is unknown, through the
information acquired from a group of advisor agents who had direct experience with
the target agent, subject to possible trust uncertainty.
Each entity in a social network can be represented as an agent who is interacting
with its network of trustees, which we refer to as advisors, where each advisor agent
in turn is in interaction with an agent of interest, which we refer to as a target agent.
Each interaction can be considered as a trust evaluation between the trustor agent, i.e.,
the agent who trusts another entity, and the trustee agent, i.e., the agent whom is being