Self-modeling and Self-awareness

  • Kirstie L. Bellman
  • Christopher Landauer
  • Phyllis Nelson
  • Nelly Bencomo
  • Sebastian Götz
  • Peter Lewis
  • Lukas Esterle
Chapter

Abstract

The purpose of this chapter is to discuss why self-aware systems must pay special attention to self-modeling capabilities, clarify what is meant by both strong and weak self-modeling, and describe some of the defining characteristics of self-modeling. This chapter is also about self-management via run-time model creation by the operational system, explaining why systems need to build models at run time, what phenomena they need to model, and how they can build models effectively. A system that is expected to operate in a dynamic environment needs to be able to update and occasionally dramatically change its models to maintain synchrony with that environment. We describe several example systems, one rather extensively, to show how the notions apply in practice.

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References

  1. 1.
    James S. Albus, Alexander M. Meystel, Engineering of Mind: An Introduction to the Science of Intelligent Systems, Wiley (2001)Google Scholar
  2. 2.
    Daniel Barbara, J. Couto, S. Jajodia, L. Popyack, N. Wu, “ADAM: Detecting Intrusions by Data Mining”, Proceedings of the IEEE SMC Information Assurance Workshop, West Point, NY (2001)Google Scholar
  3. 3.
    Jon Barwise, The Situation in Logic, CSLI Lecture Notes No.17, Center for the Study of Language and Information, Stanford U. (1989)Google Scholar
  4. 4.
    Kirstie L. Bellman, April Gillam, Christopher Landauer, “Challenges for Conceptual Design Environments: The VEHICLES Experience”, Revue Internationale de CFAO et d’Infographie, Hermes, Paris (September 1993)Google Scholar
  5. 5.
    Kirstie Bellman and Christopher Landauer “Early Work on the BrainPatch, a Reflective Service for System of Systems Integration”, Proceedings SISSY 2015: International Workshop on Self-Improving System Integration, 08 July 2015, Grenoble, France part of ICAC2015: The 12th IEEE International Conference on Autonomic Computing, 07-10 July 2015, Grenoble, France (2015)Google Scholar
  6. 6.
    Kirstie L. Bellman, Christopher Landauer, Phyllis R. Nelson, “System Engineering for Organic Computing”, Chapter 3, pp.25-80 in Rolf P. Würtz (ed.), Organic Computing, Understanding Complex Systems Series, Springer (2008)Google Scholar
  7. 7.
    Kirstie L. Bellman, Phyllis R. Nelson, “Developing Mechanisms for Determining \(\ddot{\rm {G}}\)ood Enough ïn SORT Systems”, Proc. SORT 2011: The Second IEEE Workshop on Self-Organizing Real-Time Systems, 31 Mar 2011, part of ISORC 2011, 28-31 Mar 2011, Newport Beach, California (2011)Google Scholar
  8. 8.
    Kirstie Bellman, Phyllis Nelson, Christopher Landauer, “Active Experimentation and Computational Reflection for Design and Testing of Cyber-Physical Systems” (poster), Proceedings CSD&M 2014: The Fifth International Conference on Complex Systems Design & Management, 12-14 November 2014, Paris, France (2014)Google Scholar
  9. 9.
    Gordon Blair, Nelly Bencomo, and Robert B. France, “Models@run.time”, Introduction to Special Issue of IEEE Computer, p.22-27 (October 2009)Google Scholar
  10. 10.
    C. A. Bolstad, A. M. Costello, M. R. Endsley, “Bad situation awareness designs: What went wrong and why”, International Ergonomics Association 16th World Congress, Maastricht, Netherlands (2006)Google Scholar
  11. 11.
    Christopher J. C. Burges, Dimension Reduction: A Guided Tour, Now Publishing (2010)Google Scholar
  12. 12.
    Shang-Wen Cheng, David Garlan, Bradley Schmerl, “Making Self-Adaptation an Engineering Reality”, in Ozalp Babaoglu, Mrk Jelasity, Alberto Montresor, Christof Fetzer, Stefano Leonardi, Aad van Moorsel, and Maarten van Steen (eds.), Self-Star Properties in Complex Information Systems, LNCS 3460, Springer (2005)Google Scholar
  13. 13.
    Frederica Darema, “Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements”, p.662-669 in Marian Bubak, G.Dick van Albada, Peter M.A. Sloot, Jack J. Dongarra (eds.), Proceedings ICCS 2004, Part III, 06-09 June, 2004, Krakow, Poland, SLNCS 3038, Springer (2004)Google Scholar
  14. 14.
    M.C. Dominguez, E. Vidulich, E. Vogel and G. McMillan, “ Situation awareness: Papers and annotated bibliography”, Armstrong Laboratory, Human System Center, ref. AL/CF-TR-1994-0085, 1994Google Scholar
  15. 15.
    Richard J. Doyle, “Relations Between Resilience and Validation”, (presented to) Workshop on Resilience Space Systems 01 August 2012, Keck Institute for Space Studies, Caltech (2012)Google Scholar
  16. 16.
    Clint Eastwood, quote from Magnum Force, (1973)Google Scholar
  17. 17.
    J. Everist, T.N. Mundhenk, C. Landauer, K. Bellman “Visual Surveillance Coverage: Strategies and Metrics”, Proceedings of SPIE Conference on Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision, Boston (2005)Google Scholar
  18. 18.
    Erik M. Fredericks, Betty H. C. Cheng, “Automated Generation of Adaptive Test Plans for Self-Adaptive Systems”, p. 157-167 in Proceedings SEAMS@ICSE 2015: The 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, 1819 May 2015, Firenze, Italy (2015)Google Scholar
  19. 19.
    David Garlan and Bradley Schmerl, “Using Architectural Models at Runtime: Research Challenges”, Proc. First European Workshop on Software Architectures, 21-22 May 2004, St. Andrews, Scotland, p.200-205 in Flavio Oquendo, Brian Warboys, Ron Morrison (eds.), Software Architecture, LNCS 3047, Springer (2004)Google Scholar
  20. 20.
    Eleanor J. Gibson, and Anne D. Pick An Ecological Approach to Perceptual Learning and Development, Oxford University Press (2000)Google Scholar
  21. 21.
    Rick Hayes-Roth, Hyperbeings: How Intelligent Organizations Attain Supremacy Through Information Superiority, Booklocker (2006)Google Scholar
  22. 22.
    Colin de la Higuera, Grammatical Inference: Learning Automata and Grammars, Cambridge, Cambridge University Press (2010)CrossRefGoogle Scholar
  23. 23.
    L. Itti, C. Koch, “Computational Modelling of Visual Attention”, Nature Reviews Neuroscience, Vol. 2, No. 3, pp. 194-203 (Mar 2001)CrossRefGoogle Scholar
  24. 24.
    Gabor Karsai, Miklos Maroti, Akos Ledeczi, Jeff Gray, “Composition and Cloning in Modeling and Meta-Modeling”, IEEE Transactions on Control Systems Technology, Vol. 12, No. 2, p.263-278 (March 2004)CrossRefGoogle Scholar
  25. 25.
    Jeffrey O. Kephart, David M. Chase, “The Vision of Autonomic Computing”, IEEE Computer, Vol.36, Issue 1, p.41-50 (Jan 2003)CrossRefGoogle Scholar
  26. 26.
    Christopher Landauer, “Abstract Infrastructure for Real Systems: Reflection and Autonomy in Real Time”, Proceedings SORT 2011: The Second IEEE Workshop on Self-Organizing Real-Time Systems, 31 March 2011, Newport Beach, California (2011) Morgan Kaufmann (1993)Google Scholar
  27. 27.
    Christopher Landauer, Kirstie L. Bellman, “Generic Programming, Partial Evaluation, and a New Programming Paradigm”, Chapter 8, pp. 108-154 in Gene McGuire (ed.), Software Process Improvement, Idea Group Publishing (1999)Google Scholar
  28. 28.
    Christopher Landauer, Kirstie L. Bellman, “Self-Modeling Systems”, p. 238-256 in R. Laddaga, H. Shrobe (eds.), “Self-Adaptive Software”, LNCS 2614, Springer (2002)Google Scholar
  29. 29.
    Christopher Landauer, Kirstie Bellman, “Designing Cooperating Self-Improving Systems”, Proc. 2015 SISSY Workshop: Self-improving Systems of Systems; 07 Jul 2015, Grenoble, France part of ICAC 2015: the 2015 Intern. Conf. on Autonomic Computing, 07-10 Jul 2015, Grenoble, France (2015)Google Scholar
  30. 30.
    Pattie Maes, “Concepts and Experiments in Computational Reflection”, pp.147-155 in Proceedings OOPSLA ’87 (1987)Google Scholar
  31. 31.
    A. Marazzi, P. Gamba, A. Mecocci, and E. Costamagna, “A mixed fractal/wavelet based approach for characterization of textured remote sensing images”, Proceedings IGARSS’97: The IEEE International Geoscience and Remote Sensing Symposium, Vol. 2, p.655-657 (1997)Google Scholar
  32. 32.
    T. N. Mundhenk, J. Everist, C. Landauer, L. Itti, K. Bellman, “Distributed biologically-based real-time tracking in the absence of prior target information”, pp. 142-153 in D. P. Casasent, E. L. Hall, J. Roning (eds.) Proc. SPIE International Conference on Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision, SPIE Press (Oct 2005)Google Scholar
  33. 33.
    Phyllis Nelson, “Self-Organized Self-Improvement”, (presented to) Dagstuhl Seminar 11181 (2011)Google Scholar
  34. 34.
    Mélanie Catherine Rochoux, Vers une meilleure prévision de la propagation d’incendies de forêt : évaluation de modèles et assimilation de donnés (Towards a more comprehensive monitoring of wildfire spread : contributions of model evaluation and data assimilation strategies), Ph.D. Thesis, 21 January 2014, Ecole Centrale Paris, France (2014)Google Scholar
  35. 35.
    Bradley Schmerl, Jonathan Aldrich, David Garlan, Rick Kazman, and Hong Yan, “Discovering Architectures from Running Systems”, IEEE Trans. Software Engineering, Vol.32, No.7, p.454-466 (Jul 2006)CrossRefGoogle Scholar
  36. 36.
    P. D. Weigl, E. V. Hanson, “Observational Learning and the Feeding Behavior of the Red Squirrel: The Ontogeny of Optimization”, Ecology, Vol.6, p.213-218Google Scholar
  37. 37.
    Afra Zomorodian, “Topological Data Analysis”, p.1-39 in Afra Zomorodian (ed.), Advances in Applied and Computational Topology, Proceedings of Symposia in Applied Math, v.70, AMS Short Course, Computational Topology, 04-05 January 2011, New Orleans, LA (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kirstie L. Bellman
    • 1
  • Christopher Landauer
    • 1
  • Phyllis Nelson
    • 2
  • Nelly Bencomo
    • 3
  • Sebastian Götz
    • 4
  • Peter Lewis
    • 3
  • Lukas Esterle
    • 5
  1. 1.Topcy House ConsultingThousand OaksUSA
  2. 2.California State Polytechnic UniversityPomonaUSA
  3. 3.Aston UniversityBirminghamUK
  4. 4.TU DresdenDresdenGermany
  5. 5.Alpen-Adria-Universität KlagenfurtKlagenfurtAustria

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