A Methodology to Engineer and Validate Dynamic Multi-level Multi-agent Based Simulations

This article proposes a methodology to model and simulate complex systems, based on IRM4MLS, a generic agent-based meta-model able to deal with multi-level systems. This methodology permits the engineering of dynamic multi-level agent-based models, to represent complex systems over several scales and domains of interest. Its goal is to simulate a phenomenon using dynamically the lightest representation to save computer resources without loss of information. This methodology is based on two mechanisms: (1) the activation or deactivation of agents representing different domain parts of the same phenomenon and (2) the aggregation or disaggregation of agents representing the same phenomenon at different scales.

[1]  Jacques Ferber,et al.  Weak Interaction and Strong Interaction in Agent Based Simulations , 2003, MABS.

[2]  Philippe Mathieu,et al.  An Interaction-Oriented Model for Multi-Scale Simulation , 2011, IJCAI.

[3]  IEEE Standard for Modeling and Simulation (M&S) High Level Architecture (HLA) — Framework and Rules , 2001 .

[4]  Laurent Navarro,et al.  Dynamic level of detail for large scale agent-based urban simulations , 2011, AAMAS.

[5]  Chih-Chun Chen,et al.  Identifying Multi-Level Emergent Behaviors in Agent-Directed Simulations using Complex Event Type Specifications , 2010, Simul..

[6]  Javier Gil-Quijano.  From biological to urban cells: lessons from three multilevel agent-based models , 2010 .

[7]  Bernard P. Zeigler,et al.  Theory of modeling and simulation , 1976 .

[8]  Stéphane Galland,et al.  Holonic multilevel simulation of complex systems: Application to real-time pedestrians simulation in virtual urban environment , 2008, Simul. Model. Pract. Theory.

[9]  Guillaume Hutzler,et al.  Automatic characterization of emergent phenomena in complex systems , 2010 .

[10]  Gildas Morvan,et al.  Multi-agent Multi-level Modeling - A methodology to Simulate Complex Systems , 2011, ANSS 2011.

[11]  Gildas Morvan,et al.  IRM4MLS: The Influence Reaction Model for Multi-Level Simulation , 2010, MABS.

[12]  Danny Weyns,et al.  Model for Simultaneous Actions in Situated Multi-agent Systems , 2003, MATES.

[13]  Jacques Ferber,et al.  MASQ - Towards an Integral Approach to Agent-Based Interaction , 2009, AAMAS 2009.

[14]  David Stuart Robertson,et al.  Enacting the Distributed Business Workflows Using BPEL4WS on the Multi-agent Platform , 2005, MATES.

[15]  Gildas Morvan,et al.  Multi-level agent-based modeling with the Influence Reaction principle , 2012, ArXiv.

[16]  Gildas Morvan,et al.  Multi-level agent-based modeling - Bibliography , 2012, ArXiv.

[17]  Rémy Courdier,et al.  See Emergence as a Metaknowledge - A Way to Reify Emergent Phenomena in Multiagent Simulations? , 2009, ICAART.

[18]  Philippe Caillou,et al.  SimAnalyzer: automated description of groups dynamics in agent-based simulations , 2012, AAMAS.

[19]  Alexis Drogoul,et al.  A Modelling Language to Represent and Specify Emerging Structures in Agent-Based Model , 2010, PRIMA.

[20]  Christopher D. Clack,et al.  A formalism for multi-level emergent behaviours in designed component-based systems and agent-based simulations , 2009 .

[21]  Paul K. Davis,et al.  Families of models that cross levels of resolution: issues for design, calibration and management , 1993, WSC '93.

[22]  J. Ferber,et al.  Influences and Reaction : a Model of Situated Multiagent Systems , 2001 .

[23]  Luc Bongaerts,et al.  Reference architecture for holonic manufacturing systems: PROSA , 1998 .

[24]  Xiao Zhou,et al.  Automated observation of multi-agent based simulations A statistical analysis approach , 2012, Stud. Inform. Univ..

[25]  Bruce Edmonds,et al.  Multi-Agent-Based Simulation III , 2003, Lecture Notes in Computer Science.

[26]  Tibor Bosse,et al.  Multi-Agent-Based Simulation XI , 2010, Lecture Notes in Computer Science.

[27]  Michael Winikoff,et al.  Principles and Practice of Multi-Agent Systems , 2012, Multiagent Grid Syst..

[28]  Fabien Michel,et al.  The IRM4S model: the influence/reaction principle for multiagent based simulation , 2007, AAMAS '07.