Abstract
Two factors can confound the interpretation of an MAS application or model. First, the MAS’s dynamics interact in complex ways with those of its environment, and agent engineers need to distinguish the two. Second, “mean field” approximations of the behavior of the system may be useful for qualitative examination of the dynamics, but can differ surprisingly from the behavior that emerges from the interactions of discrete agents. This paper examines these effects in the context of a project applying synthetic pheromones for agent-based control to a military air operations scenario.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bonabeau, E., Dorigo, M., and Theraulaz, G. Swarm Intelligence: From Natural to Artificial Systems. New York, Oxford University Press, 1999.
Bonabeau, E., Henaux, F., Guérin, S., Snyers, D., Kuntz, P., and Theraulaz, G. Routing in Telecommunications Networks with “Smart” Ant-Like Agents. Santa Fe Institute, Santa Fe, NM, 1998.
Brueckner, S. Return from the Ant: Synthetic Ecosystems for Manufacturing Control. Thesis at Humboldt University Berlin, Department of Computer Science, 2000.
Cabri, G., Leonardi, L., and Zambonelli, F. Context-dependency in Internet-agent Coordination. In Omicini, A., Tolksdorf, R., and Zambonelli, F., Editors, Engineering Societies in the Agents’ World, Lecture Notes in AI, Springer, Berlin, 2000.
Dorigo, M., Maniezzo, V., and Colorni, A. The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 26(1):1–13, 1996.
Drogoul, A. De la Simulation Multi-Agents à la Résolution Collective de Problèmes. Thesis at University of Paris IV, 1993.
Drogoul, A. When Ants Play Chess (Or Can Strategies Emerge from Tactical Behaviors? In Proceedings of Fifth European Workshop on Modelling Autonomous Agents in a Multi-Agent World (MAAMAW.93), pages 13–27, Springer, 1995.
Ferber, J. Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Harlow, UK, Addison Wesley Longman, 1999.
Goss, S., Aron, S., Deneubourg, J. L., and Pasteels, J. M. Self-organized Shortcuts in the Argentine Ant. Naturwissenschaften, 76:579–581, 1989.
Huberman, B. A. and Glance, N. S. Evolutionary Games and Computer Simulations. Proceedings of the National Academy of Science USA, 90(August):7716–7718, 1993.
Kott, A. SEADy Storm. 2000. Web Page, http://www.cgi.com/web2/govt/seadystorm.doc.
Moro, G. and Viroli, M. On Observing and Constraining Active Systems. In Omicini, A., Tolksdorf, R., and Zambonelli, F., Editors, Engineering Societies in the Agents’ World, Lecture Notes in AI, Springer, Berlin, 2000.
Page, S. E. On Incentives and Updating in Agent Based Models. Computational Economics, 10:67–87, 1997.
Parunak, H. V. D. ‘Go to the Ant’: Engineering Principles from Natural Agent Systems. Annals of Operations Research, 75:69–101, 1997.
Parunak, H. V. D. From Chaos to Commerce: Practical Issues and Research Opportunities in the Nonlinear Dynamics of Decentralized Manufacturing Systems. In Proceedings of Second International Workshop on Intelligent Manufacturing Systems, pages k15–k25, K. U. Leuven, 1999.
Parunak, H. V. D. and Brueckner, S. Ant-Like Missionaries and Cannibals: Synthetic Pheromones for Distributed Motion Control. In Proceedings of Fourth International Conference on Autonomous Agents (Agents 2000), 2000.
Parunak, H. V. D., Kindrick, J., and Irish, B. Material Handling: A Conservative Domain for Neural Connectivity and Propagation. In Proceedings of Sixth National Conference on Artificial Intelligence, pages 307–311, American Association for Artificial Intelligence, 1987.
Parunak, H. V. D., Savit, R., and Riolo, R. L. Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users’ Guide. In Proceedings of Workshop on Multi-agent systems and Agent-based Simulation (MABS’98), pages 10–25, Springer, 1998.
Pfeifer, R. and Scheier, C. Understanding Intelligence. Cambridge, MA, MIT Press, 1999.
Shnerb, N. M., Louzoun, Y., Bettelheim, E., and Solomon, S. The importance of being discrete: Life always wins on the surface. Proc. Natl. Acad. Sci. USA, 97(19 (September 12)):10322–10324, 2000.
Steels, L. Cooperation between Distributed Agents through Self-Organization. Vrije Universiteit Brussel AI Laboratory, 1989.
White, T. and Pagurek, B. Towards Multi-Swarm Problem Solving in Networks. In Proceedings of Third International Conference on Multi-Agent Systems (ICMAS’98), pages 333–340, IEEE Computer Society, 1998.
Wilson, W. G. Resolving Discrepancies between Deterministic Population Models and Individual-Based Simulations. American Naturalist, 151(2):116–134, 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Van Dyke Parunak, H., Brueckner, S., Sauter, J., Matthews, R.S. (2000). Distinguishing Environmental and Agent Dynamics: A Case Study in Abstraction and Alternate Modeling Technologies. In: Omicini, A., Tolksdorf, R., Zambonelli, F. (eds) Engineering Societies in the Agents World. ESAW 2000. Lecture Notes in Computer Science(), vol 1972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44539-0_2
Download citation
DOI: https://doi.org/10.1007/3-540-44539-0_2
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41477-3
Online ISBN: 978-3-540-44539-5
eBook Packages: Springer Book Archive