A Control Framework for Model Continuity in JADE

This paper proposes a control framework for modelling and executing parallel/distributed multi-agent systems. The goal is to clearly separate agent behaviours (the application layer) from crosscutting control concerns (the control layer) which in general are orthogonal to a specific application and transparently affect and regulate its evolution. Different control strategies, ranging from pure concurrent to time sensitive (real-time or simulation) can be considered and applied as a plug-in to a multi-agent system. In this work the control framework is tailored to the JADE agent infrastructure, using a minimal actor computational model. JADE was chosen because it is widely used and open source, it adheres to FIPA communication standards which in turn favour application interoperability, it is based on Java. JADE, however, lacks of any built-in solution for developing time sensitive applications. This paper describes the proposed control framework in JADE and focusses on the achievement of control strategies compliant with agent mobility and resource availability. All of this favours model continuity, i.e. a seamless transformation from model analysis by simulation to model implementation and real time execution. A case study concerning a closed queue network is used for demonstrating the practical aspects of the approach. Finally, an indication of on-going and future work is provided in the conclusions.

[1]  Erol Gelenbe,et al.  A simulation framework for the investigation of adaptive behaviours in largely populated building evacuation scenarios , 2008 .

[2]  Xiaolin Hu,et al.  Model continuity in the design of dynamic distributed real-time systems , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[3]  Dipti Srinivasan,et al.  An Introduction to Multi-Agent Systems , 2010 .

[4]  P. Clarke.  GPS Satellite Surveying , 2007 .

[5]  Dirk Pawlaszczyk.  A synchronization protocol for distributed agent-based simulations with constrained optimism , 2010 .

[6]  Franco Cicirelli,et al.  Efficient environment management for distributed simulation of large‐scale situated multi‐agent systems , 2015, Concurr. Comput. Pract. Exp..

[7]  Agostino Poggi,et al.  Developing Multi-agent Systems with JADE , 2007, ATAL.

[8]  Stephen John Turner,et al.  Agent Communication in Distributed Simulations , 2004, MABS.

[9]  Saurabh Mittal,et al.  Netcentric System of Systems Engineering with DEVS Unified Process , 2013 .

[10]  Stephen John Turner,et al.  Symbiotic Simulation Control in Supply Chain of Lubricant Additive Industry , 2009, 2009 13th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications.

[11]  Michael Wooldridge,et al.  Introduction to multiagent systems , 2001 .

[12]  Franco Cicirelli,et al.  Modelling and simulation of complex manufacturing systems using statechart-based actors , 2011, Simul. Model. Pract. Theory.

[13]  M. Calisti,et al.  FOUNDATION FOR INTELLIGENT PHYSICAL AGENTS , 2000 .

[14]  Franco Cicirelli,et al.  An Agent Infrastructure over HLA for Distributed Simulation of Reconfigurable Systems and its Application to UAV Coordination* , 2009, Simul..

[15]  Demetrio Laganà,et al.  Solving simulation optimization problems on grid computing systems , 2006, Parallel Comput..

[16]  Richard M. Fujimoto,et al.  Parallel and Distribution Simulation Systems , 1999 .

[17]  Rainer Unland,et al.  Agent.GUI: A Multi-agent Based Simulation Framework , 2011, FedCSIS.

[18]  Gul A. Agha,et al.  ACTORS - a model of concurrent computation in distributed systems , 1985, MIT Press series in artificial intelligence.

[19]  Nicolas Lhuillier,et al.  FOUNDATION FOR INTELLIGENT PHYSICAL AGENTS , 2003 .

[20]  Ulrich Schmid.  Synchronized UTC for Distributed Real-Time Systems , 1994 .

[21]  R.M. Fujimoto,et al.  Parallel and distributed simulation systems , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).