Research Output
Artificial Immunology for Collective Adaptive Systems Design and Implementation
  Distributed autonomous systems consisting of large numbers of components with no central control point need to be able to dynamically adapt their control mechanisms to deal with an unpredictable and changing environment. Existing frameworks for engineering self-adaptive systems fail to account for the need to incorporate self-expression—that is, the capability of a system to dynamically adapt its coordination pattern during runtime. Although the benefits of incorporating self-expression are well known, currently there is no principled means of enabling this during system design. We propose a conceptual framework for principled design of systems that exhibit self-expression, based on inspiration from the natural immune system. The framework is described as a set of design principles and customizable algorithms and then is instantiated in three case studies, including two from robotics and one from artificial chemistry. We show that it enables self-expression in each case, resulting in systems that are able to adapt their choice of coordination pattern during runtime to optimize functional and nonfunctional goals, as well as to discover novel patterns and architectures.

  • Type:


  • Date:

    01 May 2016

  • Publication Status:


  • Publisher

    Association for Computing Machinery (ACM)

  • DOI:


  • ISSN:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004 Data processing & computer science

  • Funders:

    ASCENS project (EU FP7-FET)


Capodieci, N., Hart, E., & Cabri, G. (2016). Artificial Immunology for Collective Adaptive Systems Design and Implementation. ACM transactions on autonomous and adaptive systems, 11(2), 1-25.



Autonomic computing, artificial immune system, framework

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