A Reverse Engineering Form for Multi Agent Systems

Kubera, Yoann; Mathieu, Philippe; Picault, Sébastien

doi:10.1007/978-3-642-02562-4_8

Cited by 4 publications

(6 citation statements)

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“…On the opposite, an interaction-oriented design allows generating random emergent phenomena-i.e., generate the behavior of each agents and the interactions between them randomly-from a given interaction library. When the expected phenomenon is observed, it is possible to backtrack to the model of the simulation and to figure out what is the hypothesis [27]. It corresponds to LEIA 7 in Fig.…”

Section: Further Workmentioning

confidence: 99%

“…The most significant result is the LEIA [27] (for LEIA lets you Explore your Interactions for your Agents) simulation space explorer, which aims at reversing the design process used to build simulations. In classical simulation design, a candidate explanation of the origin of an emergent phenomenon is expressed in a model and then assessed with simulations.…”

Section: Towards An Interaction-oriented Design Of Agent Simulationsmentioning

confidence: 99%

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IODA: an interaction-oriented approach for multi-agent based simulations

Kubera

Mathieu

Picault

2011

Auton Agent Multi-Agent Syst

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Multi-Agent Systems (MAS) design methodologies and Integrated DevelopmentEnvironments exhibit many interesting properties that also support simulation design. Yet, in their current form, they are not appropriate enough to model Multi-Agent Based Simulations (MABS). Indeed, their design is focused on the functionalities to be achieved by the MAS and the allocation of these functionalities among software agents. In that context, the most important point of design is the organization of the agents and how they communicate with each other. On the opposite, MABS aim at studying emergent phenomena, the origin of which lies in the interactions between entities and their interaction with the environment. In that context, the interactions are not limited to exchanging messages but can also be fundamental physical interactions or any other actions involving simultaneously the environment and one or several agents. To deal with this issue, this paper presents the core notions of the Interaction-Oriented Design of Agent simulations (IODA) approach to simulation design. It includes a design methodology, a model, an architecture and also JEDI, a simple implementation of IODA concepts for reactive agents. First of all, our approach focuses on the design of an agent-independent specification of behaviors, called interactions. These interactions are not limited to the analysis phase of simulation: they are made concrete both in the model and at the implementation stage. In addition, no distinction is made between agents and objects: all entities of the simulation are agents. Owing to this principle, designing which interactions occur between agents, as well as how agents act, is achieved by means of an intuitive plugand-play process, where interaction abilities are distributed among the agents. Besides, the guidelines provided by IODA are not limited to the specification of the model as they help the designer from the very beginning towards a concrete implementation of the simulation.

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Section: Further Workmentioning

confidence: 99%

Section: Towards An Interaction-oriented Design Of Agent Simulationsmentioning

confidence: 99%

IODA: an interaction-oriented approach for multi-agent based simulations

Kubera

Mathieu

Picault

2011

Auton Agent Multi-Agent Syst

Self Cite

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“…Les simulations à base d'agents ont été largement utilisées pour modéliser des phénomènes économiques, géographiques ou sociaux. Il existe plusieurs plateformes de simulation, certaines plus accessibles, comme NetLogo (Wilensky, 1999), et d'autres plus ouvertes, comme par exemple Moduleco (Phan, 2004), Gama (Taillandier et al, 2010) ou Repast (North et al, 2006).Se basant sur ces plateformes, certains outils permettent l'analyse automatique de simulations, tels que l'analyseur Leia (Gaillard et al, 2008), OpenMole (Reuillon et al, 2010) et (Caillou, 2010).…”

Section: Etat De L'artunclassified

“…Calibrer les simulations signifie trouver le bon ensemble de valeurs (ou intervalles de valeurs) pour les paramètres globaux et individuels qui permettent de minimiser la différence entre comportements aggrégés simulés et observés. Cette démarche d'optimisation est mise en oeuvre dans plusieurs outils existants comme LEIA (Gaillard et al, 2008) et GAMA (Taillandier et al, 2010).…”

Section: Introductionunclassified

http://ria.revuesonline.com/article.jsp?articleId=19006

Caillou¹,

Gil-Quijano²

2013

Revue d'intelligence artificielle

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Multi agent based simulations (MABS) have been successfully exploited to model complex systems in different areas. Nevertheless a pitfall of MABS is that their complexity increases with the number of agents and the number of different types of behaviours considered in the model. For average and large systems it is impossible to validate the trajectories of single agents in a simulation. The classical validation approaches, where only global indicators are evaluated, are too simplistic to give enough confidence on the simulation's model. It is then necessary to introduce intermediate levels of validation. In this paper we propose the use of data clustering and automated characterization of clusters in order to build, describe and follow the evolution of groups of agents in simulations. The description of clusters is used to generate profiles of agents that are reintroduced in simulations in order to study the stability of the descriptions and structures of clusters over several simulations and decide their capability to describe the modelled phenomena. These tools provides the modeller with an intermediate point of view on the evolution of the model. They are flexible enough to be applied both offline and online, and we illustrate it with both a NetLogo and a CSV-simulation log example.

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“…These characteristics normally make the resolution not deterministic. As a consequence, for one considered scenario, the solutions obtained from one execution to another may be different [16]. Studying the distances between those obtained solutions give information about the stability of the system in comparison with exact approach.…”

Section: A Performance Metricsmentioning

confidence: 99%

Characterizing and Evaluating Problem Solving Self-* Systems

Kaddoum

Gleizes

Georgé

et al. 2009

2009 Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns

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http://www.emse.fr/~picard/publications/kaddoum09jfsma.pdfInternational audienceIn the last few years, the growing complexity of current applications has led to the design of self-organizing systems presenting self-* properties. Those systems are composed of several autonomous interactive entities. They behave autonomously and present interesting characteristics allowing them to handle dynamics coming from exogenous and endogenous changes. As those systems are different from classical ones, new or updated characterization and evaluation criteria are required for analyzing the contribution of self-* properties and system performance. This work is a first step for evaluating self-* systems. Its main purpose is to propose a simulation based evaluation framework which aims at guiding the evaluation of this kind of systems from the design phase all the way to the execution results: evaluation of the system at run-time, intrinsic and development methodologies characterization

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A Reverse Engineering Form for Multi Agent Systems

Cited by 4 publications

References 4 publications

IODA: an interaction-oriented approach for multi-agent based simulations

IODA: an interaction-oriented approach for multi-agent based simulations

http://ria.revuesonline.com/article.jsp?articleId=19006

Characterizing and Evaluating Problem Solving Self-* Systems

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