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Self-adaptation in Collective Self-aware Computing Systems

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Abstract

The goals of this chapter are to identify the challenges involved in self-adaptation (including learning and knowledge sharing) of multiple self-aware systems (or system collectives). We shall discuss the techniques available for dealing with the challenges identified (e.g., algorithms for conflict resolution, collective learning, and negotiation protocols), and which are appropriate given assumptions regarding the collective system architecture. We refer to notions of knowledge, learning, and adaptation; various self-awareness levels; and reference scenarios introduced in Chap. 4.

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References

  1. Jacob Beal, Jeffrey Berliner, and Kevin Hunter. Fast precise distributed control for energy demand management. In Self-Adaptive and Self-Organizing Systems (SASO), 2012 IEEE Sixth International Conference on, pages 187–192. IEEE, 2012.

    Google Scholar 

  2. Basil Becker, Dirk Beyer, Holger Giese, Florian Klein, and Daniela Schilling. Symbolic Invariant Verification for Systems with Dynamic Structural Adaptation. In Proc. of the 28th International Conference on Software Engineering (ICSE), Shanghai, China. ACM Press, 2006.

    Google Scholar 

  3. Sven Burmester, Holger Giese, Eckehard Mnch, Oliver Oberschelp, Florian Klein, and Peter Scheideler. Tool Support for the Design of Self-Optimizing Mechatronic Multi-Agent Systems. International Journal on Software Tools for Technology Transfer (STTT), 10(3):207–222, June 2008.

    Google Scholar 

  4. Lucian Busoniu, Robert Babuska, and Bart De Schutter. A comprehensive survey of multiagent reinforcement learning. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 38(2):156–172, 2008.

    Google Scholar 

  5. Caroline Claus and Craig Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National/Tenth Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence, AAAI ’98/IAAI ’98, pages 746–752, Menlo Park, CA, USA, 1998. American Association for Artificial Intelligence.

    Google Scholar 

  6. Vincent Conitzer and Tuomas Sandholm. Self-interested automated mechanism design and implications for optimal combinatorial auctions. In Proceedings of the 5th ACM conference on Electronic commerce, pages 132–141. ACM, 2004.

    Google Scholar 

  7. Constantinos Daskalakis, Paul W Goldberg, and Christos H Papadimitriou. The complexity of computing a Nash equilibrium. SIAM Journal on Computing, 39(1):195–259, 2009.

    Google Scholar 

  8. Ada Diaconescu and Jeremy Pitt. Coordination, Organizations, Institutions, and Norms in Agent Systems X, volume 9372 of Lecture Notes in Artificial Intelligence, chapter Holonic Institutions for Multi-scale Polycentric Self-governance, pages 19–35. Springer International Publishing, 1 edition, 2015.

    Google Scholar 

  9. JoseLuis Fernandez-Marquez, Giovanna Di Marzo Serugendo, Sara Montagna, Mirko Viroli, and JosepLluis Arcos. Description and Composition of Bio-inspired Design Patterns: a Complete Overview. Natural Computing, 12(1):43–67, 2013.

    Google Scholar 

  10. Dean P Foster and Rakesh V Vohra. Calibrated learning and correlated equilibrium. Games and Economic Behavior, 21(1):40–55, 1997.

    Google Scholar 

  11. Sylvain Frey, Ada Diaconescu, David Menga, and Isabelle Demeure. A holonic control architecture for a heterogeneous multi-objective smart micro-grid. In Self-Adaptive and Self-Organizing Systems (SASO), 2013 IEEE 7th International Conference on, pages 21–30. IEEE, 2013.

    Google Scholar 

  12. Sylvain Frey, François Huguet, Cédric Mivielle, David Menga, Ada Diaconescu, and Isabelle M Demeure. Scenarios for an autonomic micro smart grid. In SMARTGREENS, pages 137–140, 2012.

    Google Scholar 

  13. Theodore Geisel. The Cat in the Hat Comes Back. Random House, 1958.

    Google Scholar 

  14. Holger Giese, Sven Burmester, Florian Klein, Daniela Schilling, and Matthias Tichy. Multi-Agent System Design for Safety-Critical Self-Optimizing Mechatronic Systems with UML. In Brian Henderson-Sellers and J Debenham, editors, OOPSLA 2003 - Second International Workshop on Agent-Oriented Methodologies, pages 21–32, Anaheim, CA, USA, Center for Object Technology Applications and Research (COTAR), University of Technology, Sydney, Australia, October 2003.

    Google Scholar 

  15. Holger Giese and Wilhelm Schäfer. Model-Driven Development of Safe Self-Optimizing Mechatronic Systems with MechatronicUML. In Javier Camara, Rogrio de Lemos, Carlo Ghezzi, and AntÂnia Lopes, editors, Assurances for Self-Adaptive Systems, volume 7740 of Lecture Notes in Computer Science (LNCS), pages 152–186. Springer, January 2013.

    Google Scholar 

  16. Harry Goldingay and Peter R. Lewis. A Taxonomy of Heterogeneity and Dynamics in Particle Swarm Optimisation. In Thomas Bartz-Beielstein, Jrgen Branke, Bogdan Filipi, and Jim Smith, editors, Parallel Problem Solving from Nature PPSN XIII, volume 8672 of Lecture Notes in Computer Science, pages 171–180. Springer International Publishing, 2014.

    Google Scholar 

  17. Amy R Greenwald and Jeffrey O Kephart. Probabilistic pricebots. In Proceedings of the fifth international conference on Autonomous agents, pages 560–567. ACM, 2001.

    Google Scholar 

  18. James E Hanson, Gerald J. Tesauro, Jeffrey O Kephart, and Edward C Snible. Multi-agent implementation of asymmetric protocol for bilateral negotiations. In Proceedings of the 4th ACM Conference on Electronic Commerce, pages 224–225. ACM, 2003.

    Google Scholar 

  19. Sergiu Hart and Andreu Mas-Colell. A simple adaptive procedure leading to correlated equilibrium. Econometrica, 68(5):1127–1150, 2000.

    Google Scholar 

  20. Jin Heo and Tarek F. Abdelzaher. Adaptguard: guarding adaptive systems from instability. In Simon A. Dobson, John Strassner, Manish Parashar, and Onn Shehory, editors, Proceedings of the 6th International Conference on Autonomic Computing, ICAC 2009, June 15-19, 2009, Barcelona, Spain, pages 77–86. ACM, 2009.

    Google Scholar 

  21. Amir Jafari, Amy Greenwald, David Gondek, and Gunes Ercal. On no-regret learning, fictitious play, and nash equilibrium. In In Proceedings of the Eighteenth International Conference on Machine Learning, 2001.

    Google Scholar 

  22. J O Kephart, H Chan, R Das, D W Levine, G Tesauro, and C Lefurgy. Coordinating multiple autonomic managers to achieve specified power-performance tradeoffs. In Proceedings of the Fourth International Conference on Autonomic Computing. IEEE, 2007.

    Google Scholar 

  23. Jeffrey O Kephart. Can predictive agents prevent chaos. Economics and cognitive science.

    Google Scholar 

  24. Jeffrey O Kephart. Software agents and the route to the information economy. Proceedings of the National Academy of Sciences, 99(suppl 3):7207–7213, 2002.

    Google Scholar 

  25. Jeffrey O Kephart and David M Chess. The vision of autonomic computing. Computer, 36(1):41–50, 2003.

    Google Scholar 

  26. Jeffrey. O. Kephart, Tad Hogg, and Bernardo A. Huberman. Dynamics of computational ecosystems. Phys. Rev. A, 40:404–421, Jul 1989.

    Google Scholar 

  27. Jeffrey O Kephart, Tad Hogg, and Bernardo A Huberman. Collective behavior of predictive agents. Physica D: Nonlinear Phenomena, 42(1):48–65, 1990.

    Google Scholar 

  28. Jeffrey O Kephart and Jonathan Lenchner. A symbiotic cognitive computing perspective on autonomic computing. In Proceedings of the 2015 IEEE International Conference on Autonomic Computing. IEEE, 2015.

    Google Scholar 

  29. Jeffrey O Kephart and Gerald J Tesauro. Pseudo-convergent q-learning by competitive pricebots. In Proc. 17th Intl Conf. Machine Learning, pages 463–470, 2000.

    Google Scholar 

  30. Jeffrey O Kephart and William E Walsh. An artificial intelligence perspective on autonomic computing policies. In Policies for Distributed Systems and Networks, 2004. POLICY 2004. Proceedings. Fifth IEEE International Workshop on, pages 3–12. IEEE, 2004.

    Google Scholar 

  31. Peter R. Lewis, Arjun Chandra, Funmilade Faniyi, Kyrre Glette, Tao Chen, Rami Bahsoon, Jim Torresen, and Xin Yao. Architectural Aspects of Self-Aware and Self-Expressive Systems: From Psychology to Engineering. Computer, 48(8), August 2015.

    Google Scholar 

  32. Peter R. Lewis, Paul Marrow, and Xin Yao. Resource Allocation in Decentralised Computational Systems: An Evolutionary Market Based Approach. Autonomous Agents and Multi-Agent Systems, 21(2):143–171, 2010.

    Google Scholar 

  33. Fernando Perez-Diaz, Ruediger Zillmer, and Roderich Groß. Firefly-inspired synchronization in swarms of mobile agents. In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS ’15, pages 279–286, Richland, SC, 2015. International Foundation for Autonomous Agents and Multiagent Systems.

    Google Scholar 

  34. Ramya Raghavendra, Parthasarathy Ranganathan, Vanish Talwar, Zhikui Wang, and Xiaoyun Zhu. No power struggles: Coordinated multi-level power management for the data center. In ACM SIGARCH Computer Architecture News, volume 36, pages 48–59. ACM, 2008.

    Google Scholar 

  35. Tuomas Sandholm. Algorithm for optimal winner determination in combinatorial auctions. Artificial intelligence, 135(1):1–54, 2002.

    Google Scholar 

  36. H. Van Dyke Parunak, Sven Brueckner, Mitch Fleischer, and James Odell. A Design Taxonomy of Multi-agent Interactions. In Paolo Giorgini, JrgP. Mller, and James Odell, editors, Agent-Oriented Software Engineering IV, volume 2935 of Lecture Notes in Computer Science, pages 123–137. Springer Berlin Heidelberg, 2004.

    Google Scholar 

  37. William Vickrey. Counterspeculation, auctions, and competitive sealed tenders. The Journal of finance, 16(1):8–37, 1961.

    Google Scholar 

  38. William E Walsh, Gerald Tesauro, Jeffrey O Kephart, and Rajarshi Das. Utility functions in autonomic systems. In Autonomic Computing, 2004. Proceedings. International Conference on, pages 70–77. IEEE, 2004.

    Google Scholar 

  39. M P Wellman, W E Walsh, P R Wurman, and J K MacKie-Mason. Auction protocols for decentralized scheduling. Games and economic behavior, 35(1):271–303, 2001.

    Google Scholar 

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Author information

Authors and Affiliations

  1. IBM Thomas J Watson Research Center, 1101 Kitchawan Rd., Yorktown Heights, NY, 10598, USA

    Jeffrey O. Kephart

  2. Telecom ParisTech, CNRS LTCI, Paris Saclay University, 46 Rue Barrault, 75013, Paris, France

    Ada Diaconescu

  3. Vienna University of Technology, Treitlstrasse 3, 1030, Vienna, Austria

    Lukas Esterle

  4. Hasso-Plattner-Institut für Softwaresystemtechnik GmbH, Prof.-Dr.-Helmert-Str. 2-3, 14482, Potsdam, Germany

    Holger Giese

  5. Department of Automatic Control LTH, Lund University, SE-221 00, Lund, Sweden

    Anders Robertsson

  6. Computer Science Department, University of Illinois at Urbana-Champaign, 201 N. Goodwin Ave, Urbana, IL, 61801, USA

    Tarek Abdelzaher

  7. School of Engineering and Applied Science, Aston University, Birmingham, B4 7ET, UK

    Peter Lewis

  8. Department of Computing, Imperial College London, 180 Queen’s Gate, London, SW7 2AZ, UK

    Antonio Filieri

  9. Department of Computer Engineering, Vienna University of Technology, Treitlstrasse 3, 1040, Vienna, Austria

    Lukas Esterle

  10. Computing and Communications Department, Lancaster University, Lancaster, LA1 4WA, UK

    Sylvain Frey

Authors
  1. Jeffrey O. Kephart
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  2. Ada Diaconescu
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  3. Holger Giese
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  4. Anders Robertsson
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  5. Tarek Abdelzaher
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  6. Peter Lewis
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  7. Antonio Filieri
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  8. Lukas Esterle
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  9. Sylvain Frey
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Corresponding author

Correspondence to Jeffrey O. Kephart .

Editor information

Editors and Affiliations

  1. University of Würzburg, Würzburg, Germany

    Samuel Kounev

  2. Thomas J. Watson Research Center, Hawthorne, New York, USA

    Jeffrey O. Kephart

  3. University of Würzburg, Würzburg, Germany

    Aleksandar Milenkoski

  4. VMWare Inc.Futurewei Technologies, Inc. , Santa Clara, California, USA

    Xiaoyun Zhu

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Kephart, J.O. et al. (2017). Self-adaptation in Collective Self-aware Computing Systems. In: Kounev, S., Kephart, J., Milenkoski, A., Zhu, X. (eds) Self-Aware Computing Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-47474-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-47474-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47472-4

  • Online ISBN: 978-3-319-47474-8

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