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Specification and Design of Trust-Based Open Self-Organising Systems

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Trustworthy Open Self-Organising Systems

Part of the book series: Autonomic Systems ((ASYS))

Abstract

In open multi-agent systems, we can make only little assumptions about the system’s scale, the behaviour of participating agents, and its environment. Especially with regard to mission-critical systems, the ability to deal with a large number of heterogeneous agents that are exposed to an uncertain environment becomes a major concern: Because failures can have massive consequences for people, industries, and public services, it is of utmost importance that such systems achieve their goals under all circumstances. A prominent example are power management systems whose paramount goal is to balance production and consumption. In this context, we tackle challenges comprising how to specify and design these systems to allow for their efficient and robust operation. Among other things, we introduce constraint-based specification techniques to address the system’s heterogeneity and show trust models that allow to measure, anticipate, and deal with uncertainties. On this basis, we present algorithms for self-organisation and self-optimisation that enable the formation of scalable system structures at runtime and allow for efficient and robust resource allocation under adverse conditions. Throughout the chapter, the problem of balancing production and consumption in decentralised autonomous power management systems serves as a case study.

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Notes

  1. 1.

    http://posomas.isse.de

  2. 2.

    Note that we specify the output of non-dispatchable power plants to be part of the system’s demand. That is because their supply cannot be controlled.

  3. 3.

    Since dispatchable prosumers show discrete modes of operation (e.g. on/off), the knapsack problem (i.e. choosing which prosumers should contribute at all) can be seen as a special case of the scheduling problem.

  4. 4.

    http://www.gesetze-im-internet.de/eeg_2014/index.html

  5. 5.

    An AVPP’s local residual load is negative, for instance, if its local environment only consists of photovoltaics, leading to a surplus of production.

  6. 6.

    “Subordinate” power plants are those an AVPP is directly responsible for, i.e. those on its next lower level in the hierarchy.

  7. 7.

    Recall that b further has a minimum up-time constraint but this does not concern the generally feasible contributions relevant at this point but rather dynamic behaviour.

  8. 8.

    Piecewise linear approximations offer to formulate problems as mixed integer linear programs which have a rich and efficient algorithmic support.

  9. 9.

    Supposedly, more points were expected to provide higher accuracy.

  10. 10.

    Note that this operator leads to so-called collapsing elements [31] that need further attention [34].

  11. 11.

    We opted for anticipating prediction errors, i.e. deviations, instead of the agents’ behaviour because some agents make better prediction about their future behaviour than others and anticipating the agents’ actual behaviour without incorporating their prediction requires additional information which makes the task of creating an adequate probabilistic model much more difficult.

  12. 12.

    Bear in mind that model abstraction requires additional computational effort (see Sect. 2.3.2).

  13. 13.

    Note that we do not use TBSTs to reflect uncertainties in the dispatchable agents’ supply. If we used TBSTs, we would be faced with the problem that changing the contribution of a proposer a i in response to the TBST of a proposer a j could cause a change in a i ’s TBST, whereupon a j would have to change its contribution and so on and so forth. It is not guaranteed that there is a fixed point, i.e. a steady state.

  14. 14.

    Supported by a separate partition that holds all outliers.

  15. 15.

    If the objective is to have similar mean values, optimal anticlusterings also yield partitions whose mean values correspond to the mean of all elements in the system. However, the anticlustering metric still implies another order on candidate solutions than homogeneous partitioning. As the large search space prevents us from taking optimal results for granted, anticlustering is not of use here. Homogeneous partitioning is further not limited to establishing similar mean values. This is shown by the example of forming partitions with a similar number of agents of a specific type.

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Acknowledgements

This research is partly sponsored by the research unit OC-Trust (FOR 1085) of the German Research Foundation.

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Authors and Affiliations

  1. Institute for Software and Systems Engineering, University of Augsburg, Augsburg, Germany

    Gerrit Anders, Florian Siefert, Alexander Schiendorfer, Hella Seebach, Benedikt Eberhardinger, Oliver Kosak & Wolfgang Reif

  2. Department of Computer Science and Engineering, Chalmers University of Technology | University of Gothenburg, Gothenburg, Sweden

    Jan-Philipp Steghöfer

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  1. Institute for Software and Systems Engin, University of Augsburg, Augsburg, Germany

    Wolfgang Reif

  2. Institute for Software and Systems Engin, University of Augsburg, Augsburg, Bayern, Germany

    Gerrit Anders

  3. Institute for Software and Systems Engin, University of Augsburg, Augsburg, Germany

    Hella Seebach

  4. Department of Computer Science and Engin, Chalmers University of Technology Univer, Gothenburg, Sweden

    Jan-Philipp Steghöfer

  5. Human-Centered Multimedia, University of Augsburg, Augsburg, Germany

    Elisabeth André

  6. Organic Computing Group, University of Augsburg, Augsburg, Bayern, Germany

    Jörg Hähner

  7. Institute of Systems Engineering, University of Hannover, Hannover, Germany

    Christian Müller-Schloer

  8. Systems and Networking Group, University of Augsburg, Augsburg, Germany

    Theo Ungerer

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Anders, G. et al. (2016). Specification and Design of Trust-Based Open Self-Organising Systems. In: Reif, W., et al. Trustworthy Open Self-Organising Systems. Autonomic Systems. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-29201-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-29201-4_2

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  • Publisher Name: Birkhäuser, Cham

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  • Online ISBN: 978-3-319-29201-4

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