A Model of Normative Multi-agent Systems and Dynamic Relationships

López, Fabiola López y; Lück, Michael

doi:10.1007/978-3-540-25867-4_16

Cited by 24 publications

(12 citation statements)

References 14 publications

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“…First, it is necessary to formally describe them. Two main formalisms are usually used: deontic logic (von Wright 1951) and descriptive models (Vàzquez-Salceda 2003;Kollingbaum and Norman 2005;López y López and Luck 2004). In L.I.A.R., the second approach is used because it is more expressive (Vàzquez-Salceda 2003) and less prone to computational limitations, like undecidability or paradoxes (Meyer, Dignum, and Wieringa 1994).…”

Section: Related Workmentioning

confidence: 99%

L.I.A.R.: Achieving Social Control in Open and Decentralized Multiagent Systems

Vercouter

Müller

2010

Applied Artificial Intelligence

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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.

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Section: Related Workmentioning

confidence: 99%

L.I.A.R.: Achieving Social Control in Open and Decentralized Multiagent Systems

Vercouter

Müller

2010

Applied Artificial Intelligence

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“…On the one hand, the top-down approach, where the system designer defines the normative system statically off-line as in Electronic Institutions [16], or norms are created dynamically on-line by some agent that acts as a leader or a norm recommender [25]. On the other hand, the bottom-up approach, which analyses how norms can emerge inside a group of agents [6].…”

Section: Related Workmentioning

confidence: 99%

Norm reasoning services

2013

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Norms are used in open Multi-Agent Systems as a formal specification of deontic statements aimed at regulating the actions of agents and the interactions among them. In this paper, we propose a set of services facilitating the development of both non-normative and normative agents for norm-governed MAS. Specifically, we propose to provide agents with norm reasoning services. These services will help agent designers/developers to programme agents that consider norm reasoning without having to implement the needed mechanisms to reason about norms by themselves. This article shows how these services perform as well as the results of the experiments that we conducted to evaluate their performance.

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“…Independent components of this framework have already been presented in different forums Luck, 2003, 2004;López et al, 2002López et al, , 2004. The objective of this paper is to present the framework as a whole.…”

Section: Introductionmentioning

confidence: 99%

A normative framework for agent-based systems

López

Lück

d’Inverno

2006

Comput Math Organiz Theor

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One of the key issues in the computational representation of open societies relates to the introduction of norms that help to cope with the heterogeneity, the autonomy and the diversity of interests among their members. Research regarding this issue presents two omissions. One is the lack of a canonical model of norms that facilitates their implementation, and that allows us to describe the processes of reasoning about norms. The other refers to considering, in the model of normative multi-agent systems, the perspective of individual agents and what they might need to effectively reason about the society in which they participate. Both are the concerns of this paper, and the main objective is to present a formal normative framework for agent-based systems.

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A Model of Normative Multi-agent Systems and Dynamic Relationships

Cited by 24 publications

References 14 publications

L.I.A.R.: Achieving Social Control in Open and Decentralized Multiagent Systems

L.I.A.R.: Achieving Social Control in Open and Decentralized Multiagent Systems

Norm reasoning services

A normative framework for agent-based systems

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