A Multi-agent Simulation Framework to Support Agent Interactions under Different Domains

The ability to study complex systems has become feasible with the new intensive computing resources such as GPU, multi-core, clusters, and Cloud infrastructures. Many companies and scientific applications use multi-agent modeling and simulation platforms to study complex processes where analytical approach is not feasible. In this paper, we use two negotiation protocols to generalize the interaction behaviors between agents in multi-agent environments. The negotiation protocols are enforced by a domain-independent marketplace agent. In order to provide the agents with flexible language structure, a domain-dependent ontology is used. The integration of the domain-independent marketplace with the domain-dependent language ontology is accomplished through an automatic code generation tool. The tool simplifies deploying the framework for a specific domain of interest. Our methodology is implemented in FD-DEVS simulation environment and SES ontological framework.

[1]  R. Palmer,et al.  Asset Pricing Under Endogenous Expectations in an Artificial Stock Market , 1996 .

[2]  Mike Surridge,et al.  Negotiating for software services , 2000, Proceedings 11th International Workshop on Database and Expert Systems Applications.

[3]  Michael J. North,et al.  AGENT-BASED MODELING AND SIMULATION: DESKTOP ABMS , 2007 .

[4]  Bernard P. Zeigler,et al.  DEVS/SOA: A Cross-Platform Framework for Net-centric Modeling and Simulation in DEVS Unified Process , 2009, Simul..

[5]  Bernard P. Zeigler,et al.  Ontology-based marketplace for supporting negotiation in different scientific applications , 2012, IEEE International Conference on Systems, Man and Cybernetics.

[6]  Ratul Mahajan,et al.  Experiences applying game theory to system design , 2004, PINS '04.

[7]  Nigel P. Topham,et al.  Performance of the decoupled ACRI-1 architecture: the perfect club , 1995, HPCN Europe.

[8]  Bernard P. Zeigler,et al.  A modeling and simulation-based methodology to support dynamic negotiation for web service applications , 2012, Simul..

[9]  Duncan C. McFarlane,et al.  Designing Automated Allocation Mechanisms for Service Procurement of Imperfectly Substitutable Services , 2013, IEEE Transactions on Computational Intelligence and AI in Games.

[10]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[11]  Tim Cooper Case Studies of Four Industrial Meta-Applications , 1999, HPCN Europe.

[12]  Michael J. North,et al.  Agent-based modeling and simulation , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).

[13]  Michael Wooldridge,et al.  Game Theory and Decision Theory in Multi-Agent Systems , 2002, Autonomous Agents and Multi-Agent Systems.

[14]  W. Arthur,et al.  The Economy as an Evolving Complex System II , 1988 .

[15]  V. C. Ramesh,et al.  Intelligent agents for negotiations in market games. I. Model , 1998 .

[16]  X.-M. Yuan,et al.  Analysis of a Software Focused Supply Chain in Photo Development Market , 2006, 2006 4th IEEE International Conference on Industrial Informatics.

[17]  Bernard P. Zeigler,et al.  Reachability Graph of Finite and Deterministic DEVS Networks , 2009, IEEE Transactions on Automation Science and Engineering.

[18]  R. Palmer,et al.  Artificial economic life: a simple model of a stockmarket , 1994 .