Fabien Michel

From Agents to Organizations: An Organizational View of Multi-agent Systems

While multi-agent systems seem to provide a good basis for building complex software systems, this paper points out some of the drawbacks of classical agent centered multi-agent systems. To resolve these difficulties we claim that organization centered multi-agent system, or OCMAS for short, may be used. We propose a set of general principles from which true OCMAS may be designed. One of these principles is not to assume anything about the cognitive capabilities of agents. In order to show how OCMAS models may be designed, we propose a very concise and minimal OCMAS model called AGR, for Agent/Group/Role. We propose a set of notations and a methodological framework to help the designer to build MAS using AGR. We then show that it is possible to design multi-agent systems using only OCMAS models.

Environments for Multi-Agent Systems III

Models, Architecture, and Design.- A Reference Architecture for Situated Multiagent Systems.- A Unified Model for Physical and Social Environments.- Exploiting the Environment for Coordinating Agent Intentions.- CArtA gO: A Framework for Prototyping Artifact-Based Environments in MAS.- Mediated Interaction and Stigmery.- Environment as Active Support of Interaction.- Environmental Support for Tag Interactions.- Cognitive Stigmergy: Towards a Framework Based on Agents and Artifacts.- Trace Signals: The Meanings of Stigmergy.- Regulation Function of the Environment in Agent-Based Simulation.- Governing Environment.- Establishing Global Properties of Multi-Agent Systems Via Local Laws.- E4MAS Through Electronic Institutions.- Spatially Distributed Normative Infrastructure.- Enhancing the Environment with a Law-Governed Service for Monitoring and Enforcing Behavior in Open Multi-Agent Systems.- Applications.- Urban Traffic Control with Co-Fields.- Designing Self-organising MAS Environments: The Collective Sort Case.

Modeling dynamic environments in multi-agent simulation

Real environments in which agents operate are inherently dynamic—the environment changes beyond the agents’ control. We advocate that, for multi-agent simulation, this dynamism must be modeled explicitly as part of the simulated environment, preferably using concepts and constructs that relate to the real world. In this paper, we describe such concepts and constructs, and we provide a formal framework to unambiguously specify their relations and meaning. We apply the formal framework to model a dynamic RoboCup Soccer environment and elaborate on how the framework poses new challenges for exploring the modeling of environments in multi-agent simulation.

Integrating tools and infrastructures for generic multi-agent systems

In this paper, we present MadKit/SEdit, an agent infrastructure combined with a generic design tool for multi-agent systems. This toolkit is based on a organizational metaphor to integrate highly heterogeneous agent systems. We explain the principles of MadKit, the underlying agent platform, and show how it can integrate various agent architectures and provides structuration for multiple simultaneous systems and semantics. The architecture, based on a minimal agent runtime, agentified platform services and modular application host, is presented. The SEdit design tool, built itself as a MAS is also discussed. We present its key points in terms of multi-model support, and integration with the infrastructure, from design to maintenance. We illustrate our approach by discussing some consequences of this architecture, and describe our motivation for this design: integration and reuse, organizational patterns, and overall versatility. A summary is given of some key MadKit-based applications to date.

The IRM4S model: the influence/reaction principle for multiagent based simulation

The IRM4S model (Influence Reaction Model for Simulation) is an adaptation of the formalism of [2] for multiagent based simulations (MABS). The goal of IRM4S is to provide a framework that eases the use of the Influence/Reaction principle within MABS.

MIC * : a deployment environment for autonomous agents

This paper presents the MIC* model of autonomous agents deployment environment. A practical social software engineering framework based on AGR is also presented to show how MIC* is used to develop MAS applications.

Weak Interaction and Strong Interaction in Agent Based Simulations

This paper addresses the problem of the engineering divergence phenomenon in ABS. This problem is related to the fact that a particular conceptual model may give different outputs according to its implementation. Through two experiments, the paper shows that the implementation of the agents’ interaction is one of the factors that are involved in this phenomenon. The underlying idea of this paper is that this problem can be greatly diminished if the analysis of the conceptual model incorporates some key concepts which are crucial for the implementation. To this end, this work proposes to identify two different classes of interaction: weak interactions and strong interactions.

Generic architecture for multi-AUV cooperation based on a multi-agent reactive organizational approach

Because Autonomous Underwater Vehicles (AUVs) have limited perception and communication capabilities, designing efficient AUV flotillas is challenging. Existing solutions are often strongly related to (1) a specific kind of mission and (2) the nature of the considered AUVs. So, it is difficult to reuse these approaches when switching to another mission context. This paper proposes a generic multi-agent based layered architecture for designing and specifying AUV flotillas at a high level of abstraction, regardless of the AUVs characteristics and skills. To this end, an organizational model is used to ease and regulate interactions between heterogeneous AUVs and combined with a behavioral reactive approach for limiting communication.

EPIS: A Grid Platform to Ease and Optimize Multi-agent Simulators Running

This paper presents the work done during the first year of the EPIS project. This project deals with the process of conductingmultiple and parallelmulti agents-based simulations (MABS) on a cluster or a grid in order to generate sufficient data for scientific use (e.g. in the case of a sensibility analysis of a simulation). We provide a new, general and user-friendly approach to marry MABS and High- Performance Computing (HPC). We, thus, propose a workflow and an associated HPC infrastructure. These two permit to easily deploy a lot of simulations on a cluster without any prior parallelizing work. The method wants to be as generic as possible: no particular MABS targeted, no overhead and HPC compliance work has to be done only once. Moreover the user is guided by a web interface that handles the workflow.

Agent Environments for Multi-agent Systems --- A Research Roadmap

Ten years ago, researchers in multi-agent systems became more and more aware that agent systems consist of more than only agents. The series of workshops on Environments for Multi-Agent Systems E4MAS 2004-2006 emerged from this awareness. One of the primary outcomes of this endeavor was a principled understanding that the agent environment should be considered as a primary design abstraction, equally important as the agents. A special issue in JAAMAS 2007 contributed a set of influential papers that define the role of agent environments, describe their engineering, and outline challenges in the field that have been the drivers for numerous follow up research efforts. The goal of this paper is to wrap up what has been achieved in the past 10 years and identify challenges for future research on agent environments. Instead of taking a broad perspective, we focus on three particularly relevant topics of modern software intensive systems: large scale, openness, and humans in the loop. For each topic, we reflect on the challenges outlined 10 years ago, present an example application that highlights the current trends, and from that outline challenges for the future. We conclude with a roadmap on how the different challenges could be tackled.