Abstract
When the number of interacting agents in a multi-agent system is large, the detailed study of the dynamics of each agent tends to obfuscate the collective and possibly emergent dynamics of the multi-agent system as a whole. When the interest is on the collective properties of the multi-agent system, a statistical study of the dynamics of the states of the agents can provide a more effective perspective on the system. In particular, a statistical approach can better focus on the long-term asymptotic properties of the studied multi-agent system. The initial part of this paper outlines a framework to approach the study of the collective properties of multi-agent systems. The framework targets large and decentralized multi-agent systems in which the relevant collective properties emerge from interactions. Then, the paper exemplifies the use of the framework to study the long-term asymptotic properties of multi-agent systems in which agents interact using the symmetric gossip algorithm. The state of each agent is represented as a real number, and the use of the framework shows that all agents exponentially converge to the average of their initial states. The analytic results provided by the framework are confirmed by independent multi-agent simulations. Finally, the paper is concluded with a brief discussion of related work and an overview of future extensions.
Similar content being viewed by others
References
Adiga, A., Kuhlman, C.J., Mortveit, H.S., Vullikanti, A.K.S.: Sensitivity of diffusion dynamics to network uncertainty. J. Artif. Intell. Res. 51, 207–226 (2014)
Asensio-Marco, C., Beferull-Lozano, B.: Fast average gossiping under asymmetric links in WSNS. In: Proceedings of the \(22^{\textrm{nd}}\) European Signal Processing Conference (EUSIPCO 2014), pp. 131–135. IEEE, Los Alamitos (2014)
Bakhshi, R., Cloth, L., Fokkink, W., Haverkort, B.: Mean-field analysis for the evaluation of gossip protocols. In: Proceedings of \(6^{\textrm{th}}\) International Conference on the Quantitative Evaluation of Systems (QEST 2009), pp. 247–256. IEEE, Los Alamitos (2009)
Bellomo, N., Burini, D., Dosi, G., Gibelli, L., Knopoff, D., Outada, N., Terna, P., Virgillito, M.E.: What is life? A perspective of the mathematical kinetic theory of active particles. Math. Models Methods Appl. Sci. 31(9), 1821–1866 (2021)
Bellouquid, A., Delitala, M.: Mathematical Modeling of Complex Biological Systems. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser, Basel (2006)
Bergenti, F., Huhns, M.N.: On the use of agents as components of software systems. In: Methodologies and Software Engineering for Agent Systems: The Agent-Oriented Software Engineering Handbook, pp. 19–32. Kluwer Academic, Norwell (2004)
Bergenti, F., Ricci, A.: Three approaches to the coordination of multiagent systems. In: Proceedings of the ACM Symposium on Applied Computing (SAC 2002), pp. 367–372. ACM, New York (2002)
Bianca, C., Dogbe, C.: On the Boltzmann gas mixture equation: linking the kinetic and fluid regimes. Commun. Nonlinear Sci. Numer. Simul. 29, 240–256 (2015)
Boghosian, B.M.: Kinetics of wealth and the Pareto law. Phys. Rev. E 89(4) (2014)
Bortolussi, L., Hillston, J., Latella, D., Massink, M.: Continuous approximation of collective system behaviour: a tutorial. Perform. Eval. 70(5), 317–349 (2013)
Boudec, J.Y., McDonald, D., Mundinger, J.: A generic mean field convergence result for systems of interacting objects. In: Proceedings of the \(4^{\textrm{th}}\) International Conference on the Quantitative Evaluation of Systems (QEST 2007). IEEE, Los Alamitos (2007)
Boyd, S., Ghosh, A., Prabhakar, B., Shah, D.: Analysis and optimization of randomized gossip algorithms. In: Proceedings of the 2004 IEEE Conference on Decision and Control, pp. 5310–5315. IEEE, Los Alamitos (2004)
Boyd, S., Ghosh, A., Prabhakar, B., Shah, D.: Randomized gossip algorithms. IEEE Trans. Inf. Theory 52(6), 2508–2530 (2006)
Brauer, F.: Compartmental models in epidemiology. In: Mathematical Epidemiology. Lecture Notes in Mathematics, vol. 1945, pp. 19–79. Springer, Berlin (2008)
Bures, T., Nicola, R.D., Gerostathopoulos, I., Hoch, N., Kit, M., Koch, N., Monreale, G.V., Montanari, U., Pugliese, R., Serbedzija, N.B., Wirsing, M., Zambonelli, F.: A life cycle for the development of autonomic systems: the e-mobility showcase. In: Proceedings of the \(7^{\textrm{th}}\) IEEE International Conference on Self-Adaptation and Self-Organizing Systems Workshops (SASOW 2013), pp. 71–76. IEEE, Los Alamitos (2013)
Cardelli, L., Csikász-Nagy, A.: The cell cycle switch computes approximate majority. Sci. Rep. 2(656) (2012)
Cardelli, L., Tribastone, M., Tschaikowski, M., Vandin, A.: Syntactic Markovian bisimulation for chemical reaction networks. In: Models, Algorithms, Logics and Tools: Essays Dedicated to Kim Guldstrand Larsen on the Occasion of His 60th Birthday. Lecture Notes in Computer Science, vol. 10460, pp. 466–483. Springer, Berlin (2017)
Cardelli, L., Tribastone, M., Tschaikowski, M., Vandin, A.: Symbolic computation of differential equivalences. Theor. Comput. Sci. 777, 132–154 (2019)
Cardelli, L., Tribastone, M., Tschaikowski, M.: From electric circuits to chemical networks. Nat. Comput. 19, 237–248 (2020)
Castelli, G., Mamei, M., Rosi, A., Zambonelli, F.: Engineering pervasive service ecosystems: the SAPERE approach. ACM Trans. Auton. Adapt. Syst. 10(1), 1:1–1:27 (2015)
Chakrabarti, B.K., Chakraborti, A., Chatterjee, A.: Econophysics and Sociophysics: Trends and Perspectives. Wiley, New York (2006)
De Nicola, R., Jähnichen, S., Wirsing, M.: Rigorous engineering of collective adaptive systems: special section. Int. J. Softw. Tools Technol. Transf. 22, 389–397 (2020)
Dimarco, G., Pareschi, L., Toscani, G., Zanella, M.: Wealth distribution under the spread of infectious diseases. Phys. Rev. E 120, 022303 (2020)
Fagnani, F., Zampieri, S.: Asymmetric randomized gossip algorithms for consensus. IFAC Proc. Vol. 41(2), 9052–9056 (2008)
Fagnani, F., Zampieri, S.: Randomized consensus algorithms over large scale networks. IEEE J. Sel. Areas Commun. 26(4) (2008)
Feret, J., Danos, V., Krivine, J., Harmer, R., Fontana, W.: Internal coarse-graining of molecular systems. Proc. Natl. Acad. Sci. 106(16), 6453–6458 (2009)
Ferscha, A.: Collective adaptive systems. In: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, pp. 893–895. ACM, New York (2015)
Galam, S.: Sociophysics: A Physicist’s Modeling of Psycho-Political Phenomena. Understanding Complex Systems. Springer, Berlin (2012)
Garcia, A.F., de Lucena, C.J.P., Zambonelli, F., Omicini, A., Castro, J. (eds.): Software Engineering for Large-Scale Multi-Agent Systems, Research Issues and Practical Applications. Lecture Notes in Computer Science, vol. 2603. Springer, Berlin (2002)
Goldman, C.V., Zilberstein, S.: Decentralized control of cooperative systems: categorization and complexity analysis. J. Artif. Intell. Res. 22, 143–174 (2004)
Hillston, J., Pitt, J., Wirsing, M., Zambonelli, F.: Collective adaptive systems: qualitative and quantitative modelling and analysis. Dagstuhl Rep. 4(12), 68–113 (2014)
Huhns, M.N. (ed.): Distributed Artificial Intelligence Pitman, London (1987)
Kash, I.A., Friedman, E.J., Halpern, J.Y.: Multiagent learning in large anonymous games. J. Artif. Intell. Res. 40, 571–598 (2011)
Liboff, R.L.: Kinetic Theory: Classical, Quantum, and Relativistic Descriptions. Graduate Texts in Contemporary Physics. Springer, Berlin (2003)
Mantegna, R.N., Stanley, H.E.: An Introduction to Econophysics: Correlations and Complexity in Finance. Cambridge University Press, Cambridge (1999)
Mariani, S., Cabri, G., Zambonelli, F.: Coordination of autonomous vehicles: taxonomy and survey. ACM Comput. Surv. 54(1), 19:1–19:33 (2021)
Mitchell, M.: Complex systems: network thinking. Artif. Intell. 170, 1194–1212 (2006)
Monica, S., Bergenti, F.: An analytic study of opinion dynamics in multi-agent systems with additive random noise. In: AI*IA 2016 Advances in Artificial Intelligence. Lecture Notes in Computer Science, vol. 10037, pp. 105–117. Springer, Berlin (2016)
Monica, S., Bergenti, F.: An analytic study of opinion dynamics in multi-agent systems. Comput. Math. Appl. 73(10), 2272–2284 (2017)
Monica, S., Bergenti, F.: Opinion dynamics in multi-agent systems: selected analytic models and verifying simulations. Comput. Math. Organ. Theory 23(3), 423–450 (2017)
Monica, S., Bergenti, F., Zambonelli, F.: Toward a kinetic framework to model the collective dynamics of multi-agent systems. In: Proceedings of the \(11^{\textrm{th}}\) International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2022). Lecture Notes in Computer Science, vol. 13703, pp. 165–180. Springer, Berlin (2022)
Pareschi, L., Toscani, G.: Interacting Multiagent Systems: Kinetic Equations and Montecarlo Methods. Oxford University Press, London (2013)
Piccoli, B., Tosin, A., Zanella, M.: Model-based assessment of the impact of driver-assist vehicles using kinetic theory. Z. Angew. Math. Phys. 71, 152 (2020)
Poslad, S.: Specifying protocols for multi-agent systems interaction. ACM Trans. Auton. Adapt. Syst. 2(4), 15:1–15:24 (2007)
Pynadath, D.V., Tambe, M.: The communicative multiagent team decision problem: analyzing teamwork theories and models. J. Artif. Intell. Res. 16, 389–423 (2002)
Schweitzer, F.: Brownian Agents and Active Particles: Collective Dynamics in the Natural and Social Sciences. Synergetics. Springer, Berlin (2003)
Slanina, F.: Inelastically scattering particles and wealth distribution in an open economy. Phys. Rev. E 69, 46 (2004)
Sznajd-Weron, K., Sznajd, J.: Opinion evolution in closed community. Int. J. Mod. Phys. C 11, 1157–1166 (2000)
Thurner, S., Klimek, P., Hanel, R.: Introduction to the Theory of Complex Systems. Oxford University Press, London (2018)
van den Broek, B., Wiegerinck, W., Kappen, B.: Graphical model inference in optimal control of stochastic multi-agent systems. J. Artif. Intell. Res. 32, 95–122 (2008)
Weidlich, W.: Sociodynamics: A Systematic Approach to Mathematical Modelling in the Social Sciences. Harwood Academic, Reading (2000)
Whitby, M., Cardelli, L., Kwiatkowska, M., Laurenti, L., Tribastone, M., Tschaikowski, M.: PID control of biochemical reaction networks. IEEE Trans. Autom. Control 67(2) (2022)
Wolpert, D.H., Tumer, K.: Collective intelligence, data routing and Braess’ paradox. J. Artif. Intell. Res. 16, 359–387 (2002)
Xu, Y., Liao, E., Scerri, P., Yu, B., Lewis, M., Sycara, K.: Towards flexible coordination of large scale multi-agent teams. In: Coordination of Large-Scale Multiagent Systems, pp. 287–309. Springer, Berlin (2006)
Ygge, F., Akkermans, H.: Decentralized markets versus central control: a comparative study. J. Artif. Intell. Res. 11, 301–333 (1999)
Acknowledgements
This work was partially supported by the Italian Ministry of University and Research under the PRIN 2017 grant 2017KRC7KT for the project Fluidware and under the PRIN 2020 grant 2020TL3X8X for the project Typeful Language Adaptation for Dynamic, Interacting and Evolving Systems (T-LADIES).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Monica, S., Bergenti, F. & Zambonelli, F. A kinetic approach to investigate the collective dynamics of multi-agent systems. Int J Softw Tools Technol Transfer 25, 693–705 (2023). https://doi.org/10.1007/s10009-023-00724-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10009-023-00724-z