Abstract
Organic Computing (OC) and other research initiatives like Autonomic Computing or Proactive Computing have developed the vision of systems possessing life-like properties: they self-organize, adapt to their dynamically changing environments, and establish other so-called self-x properties, like self-healing, self-configuration, self-optimization, etc. What we are searching for in OC are methodologies and concepts for systems that allow to cope with increasingly complex networked application systems by introduction of self-x properties and at the same time guarantee a trustworthy and adaptive response to externally provided system objectives and control actions. Therefore, in OC, we talk about controlled self-organization.
Although the terms self-organization and adaptivity have been discussed for years, we miss a clear definition of self-organization in most publications, which have a technically motivated background.
In this article, we briefly summarize the state of the art and suggest a characterization of (controlled) self-organization and adaptivity that is motivated by the main objectives of the OC initiative. We present a system classification of robust, adaptable, and adaptive systems and define a degree of autonomy to be able to quantify how autonomously a system is working. The degree of autonomy distinguishes and measures external control that is exerted directly by the user (no autonomy) from internal control of a system which might be fully controlled by an observer/controller architecture that is part of the system (full autonomy). The quantitative degree of autonomy provides the basis for characterizing the notion of controlled self-organization. Furthermore, we discuss several alternatives for the design of organic systems.
- }}Benjaafar, S. and Ramakrishnan, R. 1996. Modeling, measurement, and evaluation of sequencing flexibility in manufacturing systems. Int. J. Production Resear. 34, 1195--1220.Google ScholarCross Ref
- }}Branke, J., Mnif, M., Müller-Schloer, C., Prothmann, H., Richter, U., Rochner, F., and Schmeck, H. 2006. Organic computing—Addressing complexity by controlled self-organization. In Proceedings of the 2nd International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA'06), T. Margaria, A. Philippou, and B. Steffen, Eds. 200--206. Google ScholarDigital Library
- }}Callaway, D. S., Newman, M. E. J., Strogatz, S. H., and Watts, D. J. 2000. Network robustness and fragility: Percolation on random graphs. Physical Rev. Lett. 85, 25, 5468--5471.Google ScholarCross Ref
- }}Compton, K. 2004. Flexibility measurement of domain-specific reconfigurable hardware. In Proceedings of the ACM/SIGDA 12th International Symposium on Field Programmable Gate Arrays (FPGA'04). ACM, New York, 155--161. Google ScholarDigital Library
- }}De Wolf, T. and Holvoet, T. 2005. Emergence versus self-organisation: Different concepts but promising when combined. In Engineering Self-Organising Systems, Methodologies and Applications. A. K. R. N. S. Brueckner, G. di Marzo Serugendo Eds. Lecture Notes in Computer Science, vol. 3464. Springer-Verlag, Berlin, 1--15. Google ScholarDigital Library
- }}De Wolf, T. Samaey, G., Holvoet, T., and Roose, D. 2005. Decentralized automatic computing: Analysing self-organising emergent behavior using advanced numerical methods. In Proceedings of the 2nd International Conference on Automatic Computing (ICAC'05). 52--63. Google ScholarDigital Library
- }}DFG Priority Program 1183 Organic Computing. 2005. Website. http://www.organic-computing.de/SPP. (6/07)Google Scholar
- }}Dijkstra, E. W. 1974. Self-stabilizing systems in spite of distributed control. Comm. ACM 17, 11, 643--644. Google ScholarDigital Library
- }}Dorigo, M. and Stützle, T. 2004. Ant Colony Optimization. MIT Press, Cambridge, MA. Google ScholarDigital Library
- }}Eden, A. H. and Mens, T. 2006. Measuring software flexibility. IEE Proc. Softw. 153, 3, 113--125.Google ScholarCross Ref
- }}Gershenson, C. and Heylighen, F. 2003. When can we call a system self-organizing? In Proceedings of the 7th European Conference on Advances in Artificial Life (ECAL'03). W. Banzhaf, T. Christaller, P. Dittrich, J. T. Kim, and J. Ziegler Eds., Lecture Notes in Computer Science, vol. 2801, Springer, Dortmund, Germany, 606--614.Google Scholar
- }}Hassanzadeh, P. and Maier-Speredelozzi, V. 2007. Dynamic flexibility metrics for capability and capacity. Int. J. Flex. Manufactur. Syst. 19, 3, 195--216.Google ScholarCross Ref
- }}Hestermeyer, T., Oberschelp, O., and Giese, H. 2004. Structured information processing for self-optimizing mechatronic systems. In Proceedings of the 1st International Conference on Informatics in Control, Automation and Robotics (ICINCO'04). H. Araújo, A. Vieira, J. Braz, B. Encarnação, and M. Carvalho Eds., IEEE Computer Society Press, 230--237.Google Scholar
- }}Heylighen, F. 1999. The science of self-organization and adaptivity. In The Encyclopedia of Life Support Systems. 253--280.Google Scholar
- }}Heylighen, F. and Joslyn, C. 2001. Cybernetics and second-order cybernetics. In Encyclopedia of Physical Science & Technology 3rd Ed., R. A. Meyers Ed., Vol. 4., Academic Press, New York, 155--170.Google Scholar
- }}Jalote, P. 1994. Fault Tolerance in Distributed Systems. Prentice Hall. Google ScholarDigital Library
- }}Kephart, J. O. and Chess, D. M. 2003. The vision of autonomic computing. IEEE Computer 1, 41--50. Google ScholarDigital Library
- }}Knuth, D. E. 1998. The Art of Computer Programming—Sorting and Searching 2nd Ed. Vol. 3. Addison-Wesley Longman. Google ScholarDigital Library
- }}Lucas, C. 2006. Self-organizing systems (sos) faq. http://www.calresco.org/sos/sosfaq.htm. Frequently asked questions version 2099, (6/07).Google Scholar
- }}Mnif, M. and Müller-Schloer, C. 2006. Quantitative emergence. In Proceedings of the IEEE Mountain Workshop on Adaptive and Learning Systems (SMCals'06). 78--84.Google Scholar
- }}Mnif, M., Richter, U., Branke, J., Schmeck, H., and Müller-Schloer, C. 2007. Measurement and control of self-organised behaviour in robot swarms. In Proceedings of the 20th International Conference on Architecture of Computing Systems (ARCS'07). P. Lukowicz, L. Thiele, and G. Tröster Eds., Lecture Notes in Computer Science, vol. 4415. Springer, 209--223. Google ScholarDigital Library
- }}Mühl, G., Werner, M., Jaeger, M. A., Herrmann, K., and Parzyjegla, H. 2007. On the definitions of self-managing and self-organizing systems. In Proceedings of the KiVS Workshop: Selbstorganisierende, Adaptive, Kontextsensitive verteilte Systeme (SAKS'07). T. Braun, G. Carle, and B. Stiller Eds., VDE Verlag, 291--301.Google Scholar
- }}Müller-Schloer, C. and Sick, B. 2006. Emergence in Organic Computing systems: Discussion of a controversial concept. In Proceedings of the 3rd International Conference on Autonomic and Trusted Computing (ATC'06). L. T. Yang, H. Jin, J. Ma, and T. Ungerer Eds., Lecture Notes in Computer Science, vol. 4158. Springer, 1--16. Google ScholarDigital Library
- }}Nimis, J. and Lockemann, P. 2004. Robust multi-agent systems: The transactional conversation approach. In Proceedings of the 1st International Workshop “Safety and Security in Multiagent Systems” (SASEMAS'04). M. Barley, F. Massacci, H. Mouratidis, and P. Scerri Eds., AAMAS, New York.Google Scholar
- }}Oberschelp, O., Hestermeyer, T., Kleinjohann, B., and Kleinjohann, L. 2002. Design of self-optimizing agent-based controllers. In Proceedings of the 3rd International Workshop on Agent Based Simulation. C. Urban Ed., SCS European Publishing House.Google Scholar
- }}Parunak, H. V. D. and Brueckner, S. 2001. Entropy and self-organization in multi-agent systems. In Proceedings of the 3rd International Conference on Autonomous Agents. J. P. Müller, E. Andre, S. Sen, and C. Frasson Eds., ACM Press, 124--130. Google ScholarDigital Library
- }}Polani, D. 2003. Measuring self-organization via observers. In Proceedings of the 7th European Conference on Advances in Artificial Life (ECAL'03). W. Banzhaf, T. Christaller, P. Dittrich, J. T. Kim, and J. Ziegler Eds., Lecture Notes in Computer Science, vol. 2801. Springer, 667--675.Google ScholarCross Ref
- }}Prothmann, H., Rochner, F., Tomforde, S., Branke, J., Müller-Schloer, C., and Schmeck, H. 2008. Organic control of traffic lights. In Proceedings of the 5th International Conference on Autonomic and Trusted Computing (ATC-08). C. Rong, M. G. Jaatun, F. E. Sandnes, L. T. Yang, and J. Ma Eds., Lecture Notes in Computer Science, vol. 5060. Springer, 219--233. Google ScholarDigital Library
- }}Ribock, O., Richter, U., and Schmeck, H. 2008. Using organic computing to control bunching effects. In Proceedings of the 21th International Conference on Architecture of Computing Systems (ARCS'08). U. Brinkschulte, T. Ungerer, C. Hochberger, and R. G. Spallek Eds., Lecture Notes in Computer Science, vol. 4934. Springer, 232--244. Google ScholarDigital Library
- }}Richter, U., Mnif, M., Branke, J., Müller-Schloer, C., and Schmeck, H. 2006. Towards a generic observer/controller artchitecture for organic computing. In Proceedings of Informatik für Menschen! (INFORMATIK'06). C. Hochberger and R. Liskowsky Eds., GI-Edition, Lecture Notes in Informatics, vol. P-93. Köllen Verlag, 112--119.Google Scholar
- }}Rochner, F., Prothmann, H., Branke, J., Müller-Schloer, C., and Schmeck, H. 2006. An organic architecture for traffic light controllers. In Proceedings of Informatik für Menschen! (INFORMATIK'06). C. Hochberger and R. Liskowsky Eds., GI-Edition, Lecture Notes in Informatics, vol. P-93. Köllen Verlag, 120--127.Google Scholar
- }}Schmeck, H. 2005. Organic Computing—A new vision for distributed embedded systems. In Proceedings of the 8th IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC'05). IEEE Computer Society, 201--203. Google ScholarDigital Library
- }}Scholl, A. 2001. Robuste Planung und Optimierung—Grundlagen, Konzepte und Methoden, Experimentelle Untersuchungen. Physica-Verlag, Heidelberg.Google Scholar
- }}Shalizi, C. R. and Shalizi, K. L. 2005. Quantifying self-organization in cyclic cellular automata. eprint arXiv:nlin/0507067.Google Scholar
- }}Shalizi, C. R., Shalizi, K. L., and Haslinger, R. 2004. Quantifying self-organization with optimal predictors. Physical Rev. Lett. 93, 11, 1--4.Google Scholar
- }}Shuiabi, E., Thomson, V., and Bhuiyan, N. 2005. Entropy as a measure of operational flexibility. European J. Oper. Resear. 165, 3, 696--707.Google ScholarCross Ref
- }}Slotine, J.-J. E. and Li, W. 1990. Applied Nonlinear Control. Prentice Hall.Google Scholar
- }}Sterritt, R. 2005. Autonomic Computing. Innovations Syst. Softw. Engin. 1, 1, 79--88.Google ScholarCross Ref
- }}Taguchi, G. 1993. Taguchi on Robust Technology Development—Bringing Quality Engineering Upstream. Society of Mechanical Engineers.Google Scholar
- }}Tennenhouse, D. 2000. Proactive computing. Comm. ACM 43, 43--50. Google ScholarDigital Library
- }}Weyns, D., Parunak, H. V. D., Michel, F., Holvoet, T., and Ferber, J. 2005. Environments for multiagent systems state-of-the-art and research challenges. In Proceedings of the 1st International Workshop on Environments for Multi-Agent Systems (E4MAS'04). Revised Selected Papers. Lecture Notes in Computer Science, vol. 3374. Springer, 1--47. Google ScholarDigital Library
- }}Wright, W. A. Smith, R. E., Danek, M., and Greenway, P. 2000. A measure of emergence in an adapting, multi-agent context. In Proceedings of the 6th International Conference on the Simulation of Adaptive Behaviour (SAB'00). J. Meyer, A. Berthoz, D. Floreano, H. Roitblat, and S. Wilson Eds., ISAB Press, 20--27.Google Scholar
- }}Wright, W. A., Smith, R. E., Danek, M., and Greenway, P. 2001. A generalisable measure of self-organization and emergence. In Proceedings of the International Conference on Artificial Neural Networks (ICANN'01). G. Dorffner, H. Bischof, and K. Hornik Eds., Lecture Notes in Computer Science, vol. 2130. Springer, 857--864. Google ScholarDigital Library
- }}Zadeh, L. A. 1963. On the definition of adaptivity. Proc. IEEE 51, 3, 496--470.Google ScholarCross Ref
Index Terms
- Adaptivity and self-organization in organic computing systems
Recommendations
Self-Adaptation for Robustness and Cooperation in Holonic Multi-Agent Systems
Transactions on Large-Scale Data- and Knowledge-Centered Systems IThis paper reflects a discussion at the SARC workshop, held in Venice, October 2008. This workshop addresses robustness and cooperation in holonic multi-agent systems within a context of self-organizing and self-adaptive systems. The paper first ...
Self-organization in service discovery in presence of noncooperative agents
Self-organization and cooperation of agents in open societies play an important role in the success of the service discovery process. Self-organization allows agents to deal with dynamic requirements in service demand. Moreover, in distributed ...
Decentralized approaches for self-adaptation in agent organizations
Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithmsSelf-organizing multi-agent systems provide a suitable paradigm for developing autonomic computing systems that manage themselves. Towards this goal, we demonstrate a robust, decentralized approach for structural adaptation in explicitly modeled problem ...
Comments