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Toward Robotic Socially Believable Behaving Systems - Volume II pp 13–21Cite as

Adaptive and Evolutive Algorithms: A Natural Logic for Artificial Mind

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 106))

Abstract

This paper focuses on one of the biggest challenges we face: the possibility of reproducing in an artificial agent (based on formal algorithms) some typically human capacities (based on natural logic algorithms) such as consciousness, the ability to deliberate and make moral judgments. Recent evidences arising from dynamic systems theory and statistical learning, from the psychobiology of development and molecular neuroscience are overcoming some of the fundamental assumptions of artificial intelligence and the cognitive science of the last 50 years. From the molecular level to the social one, these new approaches analyze and exploit the structure of complex causal systems physically incorporated and integrated with the environment, setting the stage for the emergence of organisms capable of adaptive flexibility and intelligent behavior.

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Author information

Authors and Affiliations

  1. Department of European and Mediterranean Cultures (DICEM), University of Basilicata, Matera, Italy

    Mauro Maldonato

  2. Department of Human Sciences (DISU), University of Basilicata, Potenza, Italy

    Silvia Dell’Orco

Authors
  1. Mauro Maldonato
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  2. Silvia Dell’Orco
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Corresponding author

Correspondence to Mauro Maldonato .

Editor information

Editors and Affiliations

  1. Department of Psychology, Seconda Università di Napoli and IIASS, Caserta, Italy

    Anna Esposito

  2. Data Sci. Inst., Faculty of Sci. & Tech., Bournemouth University, Poole, United Kingdom

    Lakhmi C. Jain

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Maldonato, M., Dell’Orco, S. (2016). Adaptive and Evolutive Algorithms: A Natural Logic for Artificial Mind. In: Esposito, A., Jain, L. (eds) Toward Robotic Socially Believable Behaving Systems - Volume II . Intelligent Systems Reference Library, vol 106. Springer, Cham. https://doi.org/10.1007/978-3-319-31053-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-31053-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31052-7

  • Online ISBN: 978-3-319-31053-4

  • eBook Packages: EngineeringEngineering (R0)

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