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Autonomic Computing pp 57–94Cite as

Sources of Inspiration for Autonomic Computing

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Part of the book series: Undergraduate Topics in Computer Science ((UTICS))

Abstract

Autonomic computing can capitalise on advancements available from several scientific fields, both within and beyond the computer science domain. This chapter provides an overview of such fields and highlights their possible contributions to autonomic computing systems. The manner in which concepts, mechanisms and processes can be adopted and reused as software engineering approaches is highlighted across this chapter.

We discuss biology as the first source of inspiration for autonomic computing. While the bio-inspired autonomicity concept is highly relevant to autonomic computing, the potential of biology to inspire this field largely surpasses this metaphor. We therefore enlarge our discussion to biological systems in general, especially nervous systems, highlighting how their implementation in different species can inspire various solutions to autonomic computing systems.

We also show how autonomic computing shares many of its goals and necessary underlying constructions with some well-established engineering and computing fields such as automated control systems, robotics, artificial intelligence and multi-­agent systems. The chapter summarises some of the most relevant concepts and approaches available from existing fields and indicates the manner in which they can be adopted to serve the autonomic computing initiative.

A number of interrelated theoretical fields provide a potentially significant link between natural and artificial autonomic systems. Areas such as complex systems theory, cybernetics, networked systems theory and artificial life have set out to decipher the inner workings of complex adaptive systems and ultimately to control or to build artificial ones. We briefly point out the relevance of such fields and the core concepts that seem most readily applicable to autonomic computing.

Certainly, the chapter cannot provide a comprehensive view of all areas relevant to autonomic computing. Rather, its purpose is to provide an (probably biased) overview of the most relevant sources of inspiration and to offer pointers towards more extensive specialty literature.

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Notes

  1. 1.

    Nash equilibria: named after mathematician John Forbes Nash (1928) who invented the theory and received the Nobel Prize in Economic Sciences in 1994.

  2. 2.

    Pareto optimality (or efficiency): named after economist Vilfredo Pareto (1848–1923), who employed the concept in the context of economic systems. In a Pareto efficient allocation, no individual can be made better off without rendering at least another individual worse off. In this state, no Pareto improvements can be made.

  3. 3.

    ‘Reasoning’ is quite loosely defined in the MAS context, as it can range from basic reactive behaviours to complicated and proactive learning, predicting and planning capacities.

  4. 4.

    This of course represents a simplification of the human nervous system.

  5. 5.

    Sometimes in conjunction with the endocrine system.

  6. 6.

    Intelligent is broadly used here to imply well-adapted behaviour for achieving a set of objectives—for example, the organism’s survival and implied subgoals, like keeping warm, eating and avoiding being eaten.

  7. 7.

    Various neuroscience sources promote different PNS divisions (e.g. placing part of the sensory division within or outside the ANS), yet a discussion on this topic is well outside the scope of this publication.

  8. 8.

    Various neuroscience sources also include the Enteric nervous subsystem as part of the ANS, yet for clarity reasons we avoid presenting this detail here.

  9. 9.

    Action potential (spike or impulse): the sequential polarisation and depolarisation of a neuron’s membrane, caused by stimuli (in a neuron’s dendrites or soma) and travelling through the neuron (soma and axon) towards its extremity (axon terminals). Importantly, only stimuli that cross a certain threshold cause the action potential to travel across the neuron, causing the neuron to ‘fire’. Once triggered, all signals have the same action potential amplitude.

  10. 10.

    The most common neurotransmitters include acetylcholine, dopamine, GABA, glutamate and serotonin.

  11. 11.

    More precisely, the nervous projections of neurons situated in the spinal cord or brain stem connect to neurons located in the autonomic ganglia. To complete the circuit, the nervous fibres of neurons in the autonomic ganglia reach and connect to the internal organs.

  12. 12.

    R. Doursat, ‘Morphogenetic engineering weds bio self-organization to human-designed systems’, PerAda Magazine, May 2011; http://www.perada-magazine.eu/view.php?source=003722-2011-05-18

  13. 13.

    John McCarthy (1927–2011): computer scientist and cognitive scientist, considered to have coined the term ‘artificial intelligence’ (AI). John McCarthy has been a key figure in the development of the artificial intelligence field, for which he received a Turing award in 1971.

  14. 14.

    ‘A system is rational if it does the ‘right thing’, given what it knows’ [29].

  15. 15.

    Alan Turing: English mathematician, logician, cryptanalyst and computer scientist. He can be considered as one of the key predecessors of artificial intelligence (AI), as he defined a vision of AI in a 1950 article called ‘Computing Machinery and Intelligence’, where he has introduced the Turing test, genetic algorithms, machine learning and reinforcement learning. He has also introduced some fundamental AI concepts in a less-known article submitted in 1948 and entitled ‘Intelligent Machinery’, but which remained unpublished during Turing’s lifetime.

  16. 16.

    Analogy inspired by Russel and Norvig’s discussion [29] on intelligent machines passing the Turing test.

  17. 17.

    Agent : from the Latin agens—(noun) advocate or pleader; (adjective) efficient, effective or powerful; also from the Latin agere—(verb) to act, to urge or to conduct (Latin dictionary—http://www.latin-dictionary.net)

  18. 18.

    IBM’s Deep Blue computer program managed to defeat the chess champion Garry Kasparov in May 1997 (http://www-03.ibm.com/ibm/history/ibm100/us/en/icons/deepblue).

  19. 19.

    IBM’s ‘Watson’ Computing System challenged and beaten Jeopardy Champions in February 2011 (IBM Jeopardy Challenge: http://techcrunch.com/tag/watson).

  20. 20.

    A well-known connectionist experiment conducted by David Rumelhart and James McClelland at the University of California at San Diego and published in 1986 consisted in training a network of 920 artificial neurons (organised in two layers of 460 neurons) to form the past tenses of English verbs.

  21. 21.

    Cognitive sciences studying the human mind are similarly split into different communities. Cognitive psychology takes a top-down, knowledge-oriented approach, focusing on internal mental processes and states, including beliefs, desires, knowledge, ideas and motivations. Conversely, cognitive neuroscience takes a bottom-up approach by studying the biological substrates, or the brain’s neural network, that underlie and enable cognition.

  22. 22.

    Society of mind (SOM): a conceptual theory about the workings of the mind and thinking, initiated by Marvin Minsky with Seymour Papert in the 1970s and later developed and published by Minsky in the ‘Society of Mind’ book, published in 1988.

  23. 23.

    Here we refer to Complexity Theory as studied in relation to complex systems. This is not to be mistaken with the field of Computational Complexity Theory—a branch of the Theory of Computation (from theoretical computer science and mathematics) that aims to classify computational problems according to their difficulty and to relate identified classes of problems to each other.

  24. 24.

    Norbert Wiener (1894–1964): American mathematician, considered as the main originator of cybernetics.

  25. 25.

    W. Ross Ashby (1903–1972): English psychiatrist, carried-out pioneering work in the cybernetics domain.

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Author information

Authors and Affiliations

  1. Laboratoire Informatique de Grenoble, Université Joseph Fourier, Grenoble, France

    Philippe Lalanda

  2. Department of Computing, Imperial College London, London, UK

    Julie A. McCann

  3. Department of Computing and Networking, Télécom ParisTech, Paris, France

    Ada Diaconescu

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  1. Philippe Lalanda
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  2. Julie A. McCann
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  3. Ada Diaconescu
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Lalanda, P., McCann, J.A., Diaconescu, A. (2013). Sources of Inspiration for Autonomic Computing. In: Autonomic Computing. Undergraduate Topics in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-5007-7_3

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  • DOI: https://doi.org/10.1007/978-1-4471-5007-7_3

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