Abstract
There are a variety of choices which need to be made when setting up a multi-agent community. In particular, which agents communicate with which, what protocols they use, and what information flows from one to another. Such design choices will affect the efficiency of the community with respect to several parameters - accuracy, speed of solution, and message load.
In this paper, we consider one class of problem which multi-agent systems engage in - service provision. Using a simple, abstract, form of this problem, we use a mathematical analysis to show that three different messaging protocols result in varying message loads, depending on certain parameters such as number of agents and frequency of request.
If the parameters are fixed, we can conclude that one of these three protocols is better than the others. However, these parameters will usually vary over time, and hence the best of the three protocols will vary. We show that the community can adopt the best protocol if each individual agent makes a local decision based on which protocol will minimise its own message load. Hence, local decisions lead to globally good behaviour. We demonstrate this both mathematically and experimentally.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
J. L. Alty, D. Griffiths, N.R. Jennings, E. H. Mamdani, A. Struthers, and M. E. Wiegand. ADEPT-Advanced Decision Environment for Process Tasks: Overview & Architecture. In Proc. BCS Expert Systems 94 Conference (Applications Track), Cambridge, UK, 359–371, 1994.
Market-Based Control: A Paradigm for distributed resource allocation. ed S.H. Clearwater. World Scientific, 1996.
dMARS product brief. http://www.aaii.oz.au/proj/dMARS-prod-brief.html
T. Finin and R. Fritzson. KQML as an Agent Communication Language. In Proceedings of the Third International Conference on Information and Knowledge Management (CIKM’94), ACM Press, November 1994.
M.R. Genesereth and S.P. Ketchpel. Software Agents. Communications of the ACM, 37:7, 48–53, 1994.
C. Gu and T. Ishida. Analyzing the social behavior of the contract net protocol. Agents Breaking Away, Proc. MAAMAW 96. pp 116–127, 1996.
B. Hayes-Roth. A Blackboard Architecture for Control. Artificial Intelligence Journal 26, pp 21–321. 1985
H. Jean. JATlite overview. http://java.stanford.edu/java_agent/html/
L.V. Leao and S.N. Talukdar. An Environment for rule-based blackboards and distributed problem solving. International Journal for Artificial Intelligence in Engineering, 1(2): 70–79, 1986.
H.V.D. Parunak. Applications of Distributed Artificial Intelligence to Industry. In Foundations of Distributed Artificial Intelligence. Ed G.M.P. O’Hare and N.R. Jennings. Wiley Interscience, 1996.
T. Sandholm. Agents in Electronic Markets. Tutorial notes, Autonomous Agents 97 conference.
A. Sloman. The SIM_AGENT toolkit. http://www.cs.bham.ac.uk/~axs/cog_affect/sim_agent.html
R.G. Smith. The contract net protocol: high-level communication and control in a distributed problem solver. IEEE Trans. Comput., 29, 1104–1113, 1980.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Preist, C., Pearson, S. (2002). An Adaptive Choice of Messaging Protocol in Multi Agent Systems. In: d’Inverno, M., Luck, M., Fisher, M., Preist, C. (eds) Foundations and Applications of Multi-Agent Systems. Lecture Notes in Computer Science(), vol 2403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45634-1_14
Download citation
DOI: https://doi.org/10.1007/3-540-45634-1_14
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43962-2
Online ISBN: 978-3-540-45634-6
eBook Packages: Springer Book Archive