Abstract
This research paper gives an overview of several clustering methods and presents their application in formation and reconfiguration of coalitions of agents cooperating in dynamically evolving environment. Our experimental generator of coalitional structures takes into account both the stability of resulting coalitions and efficiency of computations. It focuses on providing average-case optimal solution and generates coherent stable groups with respect to agents beliefs, intentions, capabilities as well as the current environmental state. Clustering based approach leads to a robust adaptation of existing structure in response to changing environmental conditions, even in case of complex, high-dimensional models. Among numerous future research challenges listed in the last section, an adaptive approach based on evolutionary models is outlined.
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
Alsabti, K., Ranka, S., Singh, V.: Efficient K-Means Clustering Algorithm. In: Proceedings of First Workshop on High-Performance Data Mining (1998)
Ciesielski, K.: Data mining applications in multi-agent system reconfiguration. MSc. Thesis, Institute of Informatics, Warsaw University (2003)
Ciesielski, K., Draminski, M., Klopotek, M.A., Kujawiak, M., Wierzchon, S.T.: On Some Clustering Algorithms for Document Maps Creation. In: Proceedings of Intelligent Infomation Systems 2005 Conference (IIS:IIPWM 2005), Gdansk, Poland (2005) (to appear)
Goldberg, D.E.: Genetic Algorithms in Search, Optimisation and Machine Learning. Addison-Wesley, Reading (1995)
Dunin-Kęplicz, B., Verbrugge, R.: A reconfiguration algorithm for distributed problem solving. Engineering Simulation 18 (2001)
Dunin-Kęplicz, B., Verbrugge, R.: Collective intentions. Fundamenta Informaticae 51(3) (2002)
Dunin-Kęplicz, B., Verbrugge, R.: Calibrating collective commitments. In: Mařík, V., Müller, J.P., Pěchouček, M. (eds.) CEEMAS 2003. LNCS (LNAI), vol. 2691, p. 73. Springer, Heidelberg (2003)
Dunin-Kęplicz, B., Verbrugge, R.: Evolution of collective commitments during teamwork. Fundamenta Informaticae 56(4) (2003)
Ferber, J.: Multiagent Systems: An Introduction To Distributed Artificial Intelligence. Addison-Wesley, Reading (1999)
Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid. The International Journal of High Performance Computing Applications 15(3) (2001)
Fritzke, B.: A self-organizing network that can follow non-stationary distributions. In: Proceeding of the International Conference on Artificial Neural Networks 1997. Springer, Heidelberg (1997)
Jennings, N.R., Sycara, K., Woolridge, M.: A Roadmap of Agent Research and Development. Autonomous Agents and Multi-agent Systems 1 (1998)
Klusch, M., Gerber, A.: Dynamic coalition formation among rational agents. IEEE Intelligent Systems 17(3) (2002)
Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, vol. 30, Springer, Heidelberg (2001)
Mares, M.: Fuzzy coalition structures. Fuzzy Sets and Systems 114 (2000)
Ng, R.T., Han, J.: Efficient and Effective Clustering Methods for Spatial Data Mining. In: Proceedings of the 20th VLDB Conference (1994)
Rao, A., Georgeff, M.: Modeling rational agents within a BDI architecture. In: Proceedings of the 2nd Conference on Knowledge Representation and Reasoning (1991)
Sandholm, T., Lesser, V.: Coalition formation among bounded rational agents. Artificial Intelligence Journal 101(1-2) (1998)
Sandholm, T., Larson, K., Andersson, M., Shehory, O.: Coalition structure generation with worst case guarantees. Artificial Intelligence 111(12) (1999)
Shehory, O., Kraus, S.: Methods for task allocation via agent coalition formationm. Artificial Intelligence Journal 101(12) (1998)
Smith, R.G.: The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers C-29(12) (1980)
Tsvetovat, M., Sycara, K.: Customer coalitions in the electronic marketplace. In: Proceedings of the Fourth International Conference on Autonomous Agents (2000)
Weiss, G. (ed.): Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press, Cambridge (1999)
Wierzchon, S.T.: Artificial immune systems. Theory and applications (in Polish), IPI PAN Publishing House (2001)
Zhang, T., Ramakrishan, R., Livny, M.: BIRCH: Efficient Data Clustering Method for Large Databases. In: Proceedings of ACM SIGMOD International Conference on Data Management (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ciesielski, K. (2005). Towards the Adaptive Organization: Formation and Conservative Reconfiguration of Agents Coalitions. In: Gorodetsky, V., Liu, J., Skormin, V.A. (eds) Autonomous Intelligent Systems: Agents and Data Mining. AIS-ADM 2005. Lecture Notes in Computer Science(), vol 3505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492870_7
Download citation
DOI: https://doi.org/10.1007/11492870_7
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26164-3
Online ISBN: 978-3-540-31932-0
eBook Packages: Computer ScienceComputer Science (R0)