Flexibility in Organic Systems - Remarks on Mechanisms for Adapting System Goals at Runtime

Christian Becker, Jörg Hähner, Sven Tomforde

2012

Abstract

Within the last decade, technical systems that are capable of self-adaptation at runtime emerged as challenging approach to cope with the increasing complexity and interconnectedness in today’s development and management processes. One major aspect of these systems is their ability to learn appropriate responses for all kinds of possibly occurring situations. Learning requires a goal function given by the user – which is subject to modifications at runtime. In order to allow for flexible manipulations of goals within the system’s operation period, the learning component must be able to keep knowledge in order to respond to varying goals quickly. This paper describes attempts to implementing flexible learning in rule-based systems. First results show that efficient approaches are possible even in real-world applications.

References

  1. Becker, C. (2011). Untersuchung von Mechanismen zur technischen Umsetzbarkeit von Flexibilitaet in Organischen Systemen. Diploma thesis, Leibniz Universität Hannover.
  2. Benjaafar, S. and Ramakrishnan, R. (1996). Modeling, Measurement and Evaluation of Sequencing Flexibility in Manufacturing Systems. Int. J. of Production Research, 34:1195 - 1220.
  3. Compton, K. (2004). Flexibility Measurement of DomainSpecific Reconfigurable. In ACM/SIGDA Symp. on Field-Programmable Gate Arrays, pages 155 - 161.
  4. Eden, A. H. and Mens, T. (2006). Measuring software flexibility. IEE Proceedings - Software, 153(3):113-125.
  5. Grefenstette, J. J. and Ramsey, C. L. (1992). An Approach to Anytime Learning. In Proc. of the 9th Int. Workshop on Machine Learning, pages 189-195.
  6. Hassanzadeh, P. and Maier-Speredelozzi, V. (2007). Dynamic flexibility metrics for capability and capacity.
  7. Int. J. of Flexible Manufacturing Systems, 19:195 - 216.
  8. Kephart, J. O. and Chess, D. M. (2003). The Vision of Autonomic Computing. IEEE Computer, 36(1):41- 50.
  9. Kunz, T. (2003). Reliable Multicasting in MANETs. PhD thesis, Carleton University.
  10. Naujoks, B. and Beume, N. (2005). Multi-objective optimisation using S-metric selection: Application to threedimensional solution spaces. In Proc. of CEC'05, pages 1282 - 1289. IEEE.
  11. Schmeck, H. (2005a). Organic Computing. Künstliche Intelligenz, 3:68 - 69.
  12. Schmeck, H. (2005b). Organic Computing - A new vision for distributed embedded systems. In Proc. of the 8th IEEE Int. Symp. on Object-Oriented Real-Time Distributed Computing, pages 201 - 203.
  13. Schmeck, H., Müller-Schloer, C., C¸akar, E., Mnif, M., and Richter, U. (2010). Adaptivity and self-organization in organic computing systems. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 5(3):1-32.
  14. Shuiabi, E., Thomson, V., and Bhuiyan, N. (2005). Entropy as a measure of operational flexibility. European Journal of Operational Research, 165(3):696 - 707.
  15. Tennenhouse, D. (2000). Proactive computing. Communications of the ACM, 43(5):43-50.
  16. Tomforde, S. (2012). Runtime adaptation of technical systems: An architectural framework for self-configuration and self-improvement at runtime. Südwestdeutscher Verlag für Hochschulschriften. ISBN: 978-3838131337.
  17. Tomforde, S., Hurling, B., and Hähner, J. (2011). Distributed Network Protocol Parameter Adaptation in Mobile Ad-Hoc Networks. In Informatics in Control, Automation and Robotics, volume 89 of LNEE, pages 91 - 104. Springer.
  18. Williams, B. and Camp, T. (2002). Comparison of broadcasting techniques for mobile ad hoc networks. In Proc. of the 3rd ACM int. symp. on Mobile ad hoc networking & computing, pages 194-205. ACM.
  19. Wilson, S. W. (1994). ZCS: A zeroth level classifier system. Evolutionary Computation, 2(1):1-18.
  20. Wilson, S. W. (1995). Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149-175.
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Paper Citation


in Harvard Style

Becker C., Hähner J. and Tomforde S. (2012). Flexibility in Organic Systems - Remarks on Mechanisms for Adapting System Goals at Runtime . In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8565-21-1, pages 287-292. DOI: 10.5220/0004121002870292


in Bibtex Style

@conference{icinco12,
author={Christian Becker and Jörg Hähner and Sven Tomforde},
title={Flexibility in Organic Systems - Remarks on Mechanisms for Adapting System Goals at Runtime},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2012},
pages={287-292},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004121002870292},
isbn={978-989-8565-21-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Flexibility in Organic Systems - Remarks on Mechanisms for Adapting System Goals at Runtime
SN - 978-989-8565-21-1
AU - Becker C.
AU - Hähner J.
AU - Tomforde S.
PY - 2012
SP - 287
EP - 292
DO - 10.5220/0004121002870292