Engineering Hierarchical Complex Systems: An Agent-Based Approach. The Case of Flexible Manufacturing Systems

This chapter introduces a formal model to specify, model and validate hierarchical complex systems described at different levels of analysis. It relies on concepts that have been developed in the multi-agent-based simulation (MABS) literature: level, influence and reaction. One application of such model is the specification of hierarchical complex systems, in which decisional capacities are dynamically adapted at each level with respect to the emergences/constraints paradigm. In the conclusion, we discuss the main perspective of this work: the definition of a generic meta-model for holonic multi-agent systems (HMAS).

[1]  Risto Lehtonen,et al.  Multilevel Statistical Models , 2005 .

[2]  Gildas Morvan,et al.  Modélisation et conception multiniveau de systèmes complexes: Stratégie d'agentification des organisations , 2009 .

[3]  Guillaume Hutzler,et al.  Accroche-toi au niveau, j'enlève l'échelle. Éléments d'analyse des aspects multiniveaux dans la simulation à base d'agents , 2010, Rev. d'Intelligence Artif..

[4]  Kazuhiro Ohkura,et al.  Modelling of Biological Manufacturing Systems for Dynamic Reconfiguration , 1997 .

[5]  Joaquín Ezpeleta,et al.  A Banker's solution for deadlock avoidance in FMS with flexible routing and multiresource states , 2002, IEEE Trans. Robotics Autom..

[6]  Damien Trentesaux,et al.  Semi-heterarchical control of FMS: From theory to application , 2010, Eng. Appl. Artif. Intell..

[7]  Hendrik Van Brussel,et al.  Holonic Manufacturing Systems, the vision matching the problem , 1994 .

[8]  N. A. Duffie Heterarchical control of highly distributed manufacturing systems , 1996 .

[9]  Damien Trentesaux,et al.  The lifecycle of active and intelligent products: The augmentation concept , 2010, Int. J. Comput. Integr. Manuf..

[10]  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.

[11]  Angelo Lucia,et al.  Multi-scale methods and complex processes: A survey and look ahead , 2010, Comput. Chem. Eng..

[12]  H. Van Dyke Parunak,et al.  Concurrent Modeling of Alternative Worlds with Polyagents , 2006, MABS.

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

[14]  C. Jung,et al.  Method: Towards a Study of Humankind, Vol. I, Edgar Morin. 1992. Peter Lang, New York, NY. 435 pages. ISBN: 0-8204-1878-1. $63.95 , 1994 .

[15]  Luis Antunes,et al.  Multi-Agent-Based Simulation VII, International Workshop, MABS 2006, Hakodate, Japan, May 8, 2006, Revised and Invited Papers , 2007, MABS.

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

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

[18]  Mathias John,et al.  Combining micro and macro-modeling in DEVS for computational biology , 2007, 2007 Winter Simulation Conference.

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

[20]  Danny Weyns,et al.  Gradient field-based task assignment in an AGV transportation system , 2006, AAMAS '06.

[21]  Mark F. Horstemeyer,et al.  Multiscale Modeling: A Review , 2009 .

[22]  László Monostori,et al.  Emergent synthesis methodologies for manufacturing , 2001 .

[23]  Jean-Pierre Müller,et al.  Towards a Formal Semantics of Event-Based Multi-agent Simulations , 2009, MABS.

[24]  Jacques Ferber,et al.  MASQ: towards an integral approach to interaction , 2009, AAMAS.

[25]  Chrisantha Fernando,et al.  Levels of Description: A Novel Approach to Dynamical Hierarchies , 2005, Artificial Life.

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

[27]  Kilian Stoffel,et al.  Simulation modelling of ecological hierarchies in constructive dynamical systems , 2007 .

[28]  Chengxuan Cao,et al.  An algorithm for deadlock avoidance in an AGV System , 2005 .

[29]  Danny Weyns,et al.  Decentralized control of automatic guided vehicles: applying multi-agent systems in practice , 2008, OOPSLA Companion.

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

[31]  Nuno David,et al.  Multi-Agent-Based Simulation IX, International Workshop, MABS 2008, Estoril, Portugal, May 12-13, 2008, Revised Selected Papers , 2009, mAbs.

[32]  Kilian Stoffel,et al.  Modeling and simulating hierarchies using an agent-based approach , 2005 .

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

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

[35]  Harvey Goldstein,et al.  Multilevel Statistical Models: Goldstein/Multilevel Statistical Models , 2010 .

[36]  Nobutada Fujii,et al.  Modeling Biological Manufacturing Systems with Bounded-Rational Agents , 2006 .

[37]  Philippe Mathieu,et al.  Interaction-Oriented Agent Simulations: From Theory to Implementation , 2008, ECAI.

[38]  H. Van Dyke Parunak Pheromones, Probabilities, and Multiple Futures , 2010, MABS.

[39]  Danny Weyns,et al.  A Formal Model for Situated Multi-Agent Systems , 2004, Fundam. Informaticae.

[40]  Bruce H. Krogh,et al.  Deadlock avoidance in flexible manufacturing systems with concurrently competing process flows , 1990, IEEE Trans. Robotics Autom..

[41]  SallezY.,et al.  Semi-heterarchical control of FMS , 2010 .

[42]  Alexis Drogoul,et al.  Towards Virtual Experiment Laboratories: How Multi-Agent Simulations Can Cope with Multiple Scales of Analysis and Viewpoints , 1998, Virtual Worlds.