Advertisement

Systems Sciences and Cognitive Systems

  • Octavian Iordache
Part of the Understanding Complex Systems book series (UCS)

Abstract

The evolvable multi-scale engineering design is presented in correlation with general design theory. The role of meta-models for evolvable and creative conceptual design is emphasized.

The potential of active cases base reasoning systems and their interaction with designs of experiments is evaluated.

Evolvable diagnosis strategies for failure analysis and security purposes are proposed.

Manufacturing systems developments from fixed to flexible, reconfigurable and lastly evolvable with reference to assembly operations are presented. Multiple-scale agent architectures based on cognitive science studies allows integrative closure and autonomy.

Keywords

Failure Analysis Cognitive System Integrative Closure Design Cycle Categorical Framework 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications 7(1), 39–59 (1994)Google Scholar
  2. Aha, D.W., Breslow, L.A., Munoz-Avilla, H.: Conversational case-based reasoning. Applied Intelligence 14(1), 9–32 (2001)zbMATHCrossRefGoogle Scholar
  3. Baina, S., Morel, G.: Product centric holons for synchronization and interoperability in manufacturing environments. In: 12th IFAC Symposium on Information Control Problems in Manufacturing, St-Etienne, France (2006)Google Scholar
  4. Bar-Yam, Y.: When Systems Engineering Fails-Toward Complex Systems Engineering. In: International Conference on Systems, Man & cybernetics, vol. 2, pp. 2021–2028. IEEE Press, Piscataway (2003)Google Scholar
  5. Bauer, B., Kasinger, H.: AOSE and organic computing-how can they benefits from each other. Position paper. AOSE III. Springer, Heidelberg (2006)Google Scholar
  6. Benami, O., Jin, Y.: Creative stimulation in conceptual design. In: Proceedings of DETC 2002 ASME 2002 Design Engineering Technical Conference, Montreal, Canada, vol. 20, pp. 1–13 (2002)Google Scholar
  7. Black, J.: The Design of the Factory with a Future. McGraw-Hill, New York (1991)Google Scholar
  8. Braha, D., Maimon, O.: A mathematical theory of design: foundations, algorithms and applications. Kluwer, Boston (1998)zbMATHGoogle Scholar
  9. Braha, D., Reich, Y.: Topological structures for modeling engineering design processes. Res. Eng. Design 14, 185–199 (2003)CrossRefGoogle Scholar
  10. Carreras, I., Miorandi, D., Saint-Paul, R., Chlamtac, I.: Bottom-up design patterns and the energy web. IEEE Transactions on Systems, Man Cybernetics, Part A, Special issue on Engineering Cyber-Physical Systems (2009)Google Scholar
  11. Coyne, R.: Logic Models of Design. Pitman, London (1988)zbMATHGoogle Scholar
  12. Delugach, H.S.: Towards Building Active Knowledge Systems With Conceptual Graphs. In: Ganter, B., de Moor, A., Lex, W. (eds.) ICCS 2003. LNCS (LNAI), vol. 2746, pp. 296–308. Springer, Heidelberg (2003)Google Scholar
  13. Dettmer, H.W.: Strategic Navigation: A Systems Approach to Business Strategy. ASQ Quality Press (2003)Google Scholar
  14. Estrin, D., Culler, D., Pister, K., Sukhatme, G.: Connecting the physical world with pervasive networks. IEEE Pervasive Computing, 59–69 (2002)Google Scholar
  15. Frei, R., Barata, J., Di Marzo Serugendo, G.: A Complexity Theory Approach to Evolvable Production Systems. In: Sapaty, P., Filipe, J. (eds.) Proceedings of the International Workshop on Multi-Agent Robotic Systems (MARS 2007), pp. 44–53. INSTICC Press, Portugal (2007)Google Scholar
  16. Gani, R.: Chemical product design: challenges and opportunities. Comp. & Chem. Engng. 28, 2441–2457 (2004)CrossRefGoogle Scholar
  17. Goschnick, S.B.: Enacting an Agent-based Digital Self in a 24x7 Web Services World. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 187–196. Springer, Heidelberg (2003)Google Scholar
  18. Goschnick, S.B., Sterling, L.: Psychology-based Agent Architecture for Whole-of-user Interface to the Web. In: Proceedings of HF2002 Human Factors Conference: Design for the Whole Person - Integrating Physical, Cognitive and Social Aspects, Melbourne (2002)Google Scholar
  19. Grabowski, H., Rude, S., Klein, G. (eds.): Universal Design Theory. Shaker Verlag, Aachen (1998)Google Scholar
  20. IBM, An architectural blueprint for automatic computing (2005)Google Scholar
  21. Iordache, O.: Evolvable Designs of Experiments. Applications for Circuits. J. Wiley VCH, Weinheim (2009)CrossRefGoogle Scholar
  22. Jung, C.G.: Man and his symbols. Dell Publishing Company, NewYork (1997)Google Scholar
  23. Kephart, J.O., Chess, D.M.: The vision of autonomic computing. IEEE Computer 36(1), 41–50 (2003)Google Scholar
  24. Kiriyama, T., Tomiyama, T., Yoshikawa, H.: Qualitative Reasoning in Conceptual Design with Physical Features. In: Faltings, B., Struss, P. (eds.) Recent Advances in Qualitative Physics, pp. 375–386. MIT Press, Cambridge (1992)Google Scholar
  25. Lee, E.A.: Computing foundations and practice for cyber-physical systems: A preliminary report. Tech Report Univ of California Berkeley/EECS-2007-72 (2007)Google Scholar
  26. Li, S., Yang, Q.: Active CBR, Integrating case-based reasoning and active database, TR-1999-03, School of Computing Science, Simon Fraser University, Burnaby BC, Canada (1999)Google Scholar
  27. Lohse, N., Valtchanov, G., Ratchev, S., Onori, M., Barata, J.: Towards a Unified Assembly System Design Ontology using Protégé. In: Proceedings of the 8th Intl. Protégé Conference, Madrid, Spain (2005)Google Scholar
  28. Melendez, J., Colomer, J., Macaya, D.: Case based reasoning methodology for supervision. In: Procceedings of the European Control Conference, ECC 2001, Oporto, Portugal, pp. 1600–1605 (2001)Google Scholar
  29. Montani, S., Anglano, C.: Case-Based Reasoning for autonomous service failure diagnosis and remediation in software systems. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 489–503. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  30. Naumenko, A., Wegmann, A.: Two approach in system modeling and their illustration with MDA and RM-ODP. In: ICEIS 2003, the 5th International Conference on Enterprise Information System (2003)Google Scholar
  31. Nejdl, W., Froehlich, P., Schroeder, M.: A formal framework for representing diagnosis strategies in model-based diagnosis systems. In: Int. Joint Conf. on Artif. Int., IJCAI, vol. 95, pp. 1721–1727. Morgan Kaufmann Publishers, Inc., San Francisco (1995)Google Scholar
  32. Onori, M.: Evolvable Assembly Systems-A New Paradigm. In: IST 2002 33rd International Symposium on Robotics, Stockholm, pp. 617–621 (2002)Google Scholar
  33. Onori, M., Barata, J., Frei, R.: Evolvable Assembly System Basic Principles. BASYS Niagara Falls, Canada (2006)Google Scholar
  34. Pahl, P.G., Beitz, W.: Engineering design, a systematic approach. Springer, London (1996)Google Scholar
  35. Parunak, H.V.D., Brueckner, S.: Entropy and Self-Organization. In: Multi-agent Systems, Proceedings of the Fifth International Conference on Autonomous Agents, pp. 124–130. ACM Press, New York (2001)CrossRefGoogle Scholar
  36. Pattee, H.H.: Causation, control and the evolution of complexity. In: Anderson, P.B., et al. (eds.) Downward Causation, pp. 63–77. Aarhus University Press, Aarhus (2000)Google Scholar
  37. Piaget, J.: Genetic Epistemology. Columbia University Press, New York (1970)Google Scholar
  38. Piaget, J.: The construction of Reality in the Child. Ballantine Books, New York (1971)Google Scholar
  39. Rao, A., Georgeff, M.: Modelling rational agents with a BDI architecture. In: Allen, J., Fikes, R., Sandewall, E. (eds.) Proceedings of Knowledge Representation and Reasoning. Morgan Kaufman Publishers, San Mateo (1991)Google Scholar
  40. Rayudu, R.K., Samarasinghe, S., Maharaj, A.: A co-operative hybrid algorithm for fault diagnosis in power transmission. IEEE Journal of power Systems Engineering, 1939–1944 (2000)Google Scholar
  41. Reich, Y.: A critical review of General Design Theory. Res. Eng. Des. 7, 1–18 (1995)CrossRefGoogle Scholar
  42. Sakhanenko, N.A., Luger, G.F., Stern, C.R.: Managing Dynamic Contexts Using Failure-Driven Stochastic Models. In: Wilson, D., Sutcliffe, G. (eds.) Proceedings of the Florida Artificial Intelligence Research Society of AAAI, FLAIRS-2, pp. 466–472. AAAOI Press (2007)Google Scholar
  43. Schank, R.: Dynamic memory: a theory of reminding and learning in computers and people. Cambridge University Press, Cambridge (1982)Google Scholar
  44. Sisiaridis, D., Rossiter, N., Heather, M.A.: Holistic Security Architecture for Distributed Information Systems - A Categorical Approach. In: European Meeting on Cybernetics and Systems Research, Symposium Mathematical Methods in Cybernetics and Systems Theory, EMCSR-2008, University Vienna, pp. 52–57 (2008)Google Scholar
  45. Sowa, J.F.: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks Cole Publishing Co., Pacific Grove (2000)Google Scholar
  46. Sugi, M., Maeda, Y., Aiyama, Y., Harada, T., Arai, T.: A Holonic architecture for easy reconfiguration of robotic assembly systems. IEEE Trans. on Robotics and Automation 19(3), 457–564 (2003)CrossRefGoogle Scholar
  47. Takeda, H., Tomiyama, T., Yoshikawa, H., Veerkamp, P.J.: Modeling design processes. Technical Report CS-R9059, Centre for Mathematics and Computer Science (CWI), Amsterdam, Netherlands (1990)Google Scholar
  48. Takeda, H., Iino, K., Nishida, T.: Agent organization and communication with multiple ontologies. International Journal of Cooperative Information Systems 4, 321–337 (1995)CrossRefGoogle Scholar
  49. Tharumarajah, A., Wells, A.J., Nemes, L.: Comparison of the bionic, fractal and holonic manufacturing systems concepts. International Journal of Computer Integrated Manufacturing (9), 217–226 (1996)Google Scholar
  50. Tomiyama, T., Yoshikawa, H.: Extended General Design Theory. In: Design Theory for CAD, Proceedings from IFIP WG 5.2, Amsterdam (1987)Google Scholar
  51. Tomiyama, T., Kiriyama, T., Takeda, H., Xue, D.: Metamodel: A key to intelligent CAD systems. Research in Engineering Design 1(1), 19–34 (1989)CrossRefGoogle Scholar
  52. Trumler, W., Bagci, F., Petzold, J., Ungerer, T.: Towards an organic middleware for the smart dooplate project. GI Jahrestagung 2004(2), 626–630 (2004)Google Scholar
  53. Ulieru, M.: Emergence of holonic enterprises from multi-agents systems: A fuzzy evolutionary approach. In: Loia, V. (ed.) Soft Computing Agents, pp. 187–215. IOP Press, Amsterdam (2002)Google Scholar
  54. Ulieru, M., Brennan, R.W., Walker, S.S.: The holonic enterprise: a model for Internet-enabled global manufacturing supply chain and workflow management. Integrated Manufacturing Systems 13(8), 538–550 (2002)CrossRefGoogle Scholar
  55. Valckenaers, P., Van Brussel, H., Bongaerts, L., Wyns, J.: Holonic Manufacturing Systems. Integr. Comput-Aided Eng. 4(3), 191–201 (1997)Google Scholar
  56. Yoshikawa, H.: General Design Theory and a CAD system. In: Man-Machine Communications in CAD/CAM, Proceedings, IFIP W.G5.2, Tokyo, pp. 35–38. North-Holland, Amsterdam (1981)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Octavian Iordache

    There are no affiliations available

    Personalised recommendations