L.I.A.R.: Achieving social control in open and decentralized multiagent systems

Abstract

Open and decentralized multiagent systems (ODMAS) are particularly vulnerable to the introduction of faulty or malevolent agents. Indeed, such systems rely on collective tasks that are performed collaboratively by several agents that interact to coordinate themselves. It is therefore very important that agents respect the system rules, especially concerning interaction, to achieve successfully these collective tasks. In this article we propose the L.I.A.R. model to control the agents' interactions. This model follows the social control approach that consists of developing an adaptive and auto-organized control, set up by the agents themselves. As being intrinsically decentralized and nonintrusive to the agents' internal functioning, it is more adapted to ODMAS than other approaches, like cryptographic security or centralized institutions. To implement such a social control, agents should be able to characterize interaction they observe and to sanction them. L.I.A.R. includes different formalisms: (i) a social commitment model that enables agents to represent observed interactions, (ii) a model for social norm to represent the system rules, (iii) social policies to evaluate the acceptability of agents interactions, (iv) and a reputation model to enable agents to apply sanctions to their peers. This article presents experiments of an implementation of L.I.A.R. in an agentified peer-to-peer network. These experiments show that L.I.A.R. is able to compute reputation levels quickly, precisely and efficiently. Moreover, these reputation levels are adaptive and enable agents to identify and isolate harmful agents. These reputation levels also enable agents to identify good peers, with which to pursue their interactions. Copyright © 2010 Taylor & Francis Group, LLC.