A System Theoretic Approach to Constructing Test Beds for Multi-Agent Systems
As the number of multi-agent systems grows so does the need for testing agents in virtual dynamic environments. A system theoretic approach to constructing test beds for multi-agent systems is presented. The formalism is rooted in DEVS and describes agents and their environments as reflective time triggered automata. The hierarchical compositional model design is complemented by an abstract simulator to support the parallel, discrete event execution of the reflective automata.
This theoretical exploration has brought into being the simulation system JAMES, a Java-Based Agent Modeling Environment for Simulation. It constitutes a framework for constructing test beds, in which the effects of different agent strategies, e.g. referring to deliberation, mobility, and interaction, can be experimentally analyzed. The model design allows describing and embedding agents that deliberately change the overall systems' interaction and composition structure, e.g. by moving from one interaction context to another. The execution layer realizes the abstract simulator in a distributed environment. To reduce communications over the net, a move at the model level from one interaction context to another is answered by migrating processes at the execution layer. Thus in JAMES, modeling and simulation layer are coined equally by an agent-based perspective.
KeywordsCouple Model Atomic Model Discrete Event Simulation State Automaton Interaction Context
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- Bishop, M., M. Valence, and L.F. Wisniewski. Process Migration for Heterogeneous Distributed Systems. Technical Report PCSTR95–264, Dept. of Computer Science, Dartmouth College, 1995.Google Scholar
- Carmel, D. and S. Markovitch. Learning Models of Intelligent Agents. In International Joint Conference on Artificial Intelligence — IJCAI97, 1997.Google Scholar
- Chow, A.C. Parallel DEVS: A Parallel Hierarchical, Modular Modeling Formalism. SCS — Transactions on Computer Simulation, 13(2): 55–67, 1996.Google Scholar
- d’Inverno, M. D. Kinny, and M. Luck. Interaction Protocols in Agentis. In International Conference on Multi-Agent Systems ICMAS, 1998.Google Scholar
- Georgeff, M.P. and A.L. Lansky. Reactive Reasoning and Planning. In Proceeding of the Sixth Annual Conference on Artificial Intelligence AAAI-87, pages 677–682, 1987.Google Scholar
- Hanks, S., M. E. Pollack, and P. R. Cohen. Benchmarks, Test Beds, Controlled Experimentation and the Design of Agent Architectures. AAAI, (Winter): 17–42, 1993.Google Scholar
- Jennings, N. R., K. Sycara, and M. Wooldridge. A Roadmap of Agent Research and Development. Autonomous Agents and Multi-Agent Systems, 1(1): 275–306, 1998.Google Scholar
- Kür, G. Architecture of Systems Problem Solving. Plenum Press, New York, 1985.Google Scholar
- Page, E.H. Simulation Modeling Methodology: Principles and Etiology of Decision Support. PhD thesis, Virginia Polytechnic Institute and State University, 1994.Google Scholar
- Page, E.H., B.S. Canova, and J.A. Tufarolo. Web-Based Simulation in SimJava using Remote Method Invocation. In Winter Simulation Conference, Atlanta, 1997.Google Scholar
- Praehofer, H., P. Bichler, and B. P. Zeigler. Synthesis of Endomorphic Models for Event-Based Intelligent Control Employing Combined Discrete/Continuous Simulation. In Proc. on the Fourth Annual Conference on Artificial Intelligence, Simulation, and Planning in High Autonomy Systems, pages 120–127, San Diego, 1993. IEEE.Google Scholar
- Rosenschein, J.S. and L.P. Kaelbling. A Situated View of Representation and Control. Artificial Intelligence, 73, 1995.Google Scholar
- Uhrmacher, A.M. and B. Schattenberg. Agents in Discrete Event Simulation. In European Simulation Symposium — ESS ’98, Nottingham, October 1998. SCS.Google Scholar
- Uhrmacher, A.M. and K. Gugler. Distributed, Parallel Simulation of Multiple, Deliberative Agents. In Parallel and Distributed Conference PADS ’2000, Bologna, 2000.Google Scholar
- Uhrmacher, A.M. P. Tyschler, and D. Tyschler. Modeling and Simulation of Mobile Agents. Future Generation Computer Systems, (to appear).Google Scholar
- Uhrmacher, A.M. Dynamic Structures in Modeling and Simulation — A Reflective Approach. ACM Transaction on Modeling and Computer Simulation, (to appear).Google Scholar
- Zeigler, B.P. Multifaceted Modelling and Discrete Event Simulation. Academic Press, London, 1984.Google Scholar
- Zeigler, B.P. Toward A Simulation Methodology for Variable Structure Modeling. In M.S. Elzas, B.P. Zeigler, and T.I. Ören, editors, Modelling and Simulation Methodology in the Artificial Intelligence Era, pages 195–210. North Holland, Amsterdam, 1986.Google Scholar
- Zeigler, B.P., H. Praehofer, and T.G Kim. Theory of Modeling and Simulation. Academic Press, 1999.Google Scholar