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Thinking Fast and Slow in AI: The Role of Metacognition

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Machine Learning, Optimization, and Data Science (LOD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13811))

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Abstract

Artificial intelligence (AI) still lacks human capabilities, like adaptability, generalizability, self-control, consistency, common sense, and causal reasoning. Humans achieve some of these capabilities by carefully combining their thinking “fast” and “slow”. In this work we define an AI architecture that embeds these two modalities, and we study the role of a “meta-cognitive” component, with the role of coordinating and combining them, in achieving higher quality decisions.

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Acknowledgements

Nicholas Mattei was supported by NSF Awards IIS-RI-2007955, IIS-III-2107505, and IIS-RI-2134857, as well as an IBM Faculty Award and a Google Research Scholar Award. K. Brent Venable are supported by NSF Award IIS-2008011.

Author information

Authors and Affiliations

  1. Union College, Schenectady, USA

    M. Bergamaschi Ganapini

  2. IBM Research, New York, USA

    Murray Campbell, Lior Horesh, Jon Lenchner & Francesca Rossi

  3. University of Parma, Parma, Italy

    Francesco Fabiano

  4. University of Brescia, Brescia, Italy

    Andrea Loreggia

  5. Tulane University, New Orleans, USA

    Nicholas Mattei

  6. University of South Carolina, Columbia, USA

    Biplav Srivastava

  7. IHMC, Pensacola, USA

    Kristen Brent Venable

Authors
  1. M. Bergamaschi Ganapini
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  2. Murray Campbell
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  3. Francesco Fabiano
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  4. Lior Horesh
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  5. Jon Lenchner
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  6. Andrea Loreggia
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  7. Nicholas Mattei
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  8. Francesca Rossi
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  9. Biplav Srivastava
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  10. Kristen Brent Venable
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Corresponding author

Correspondence to M. Bergamaschi Ganapini .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  6. Free University of Bozen-Bolzano, Bolzano, Italy

    Giuseppe Di Fatta

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Ganapini, M.B. et al. (2023). Thinking Fast and Slow in AI: The Role of Metacognition. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13811. Springer, Cham. https://doi.org/10.1007/978-3-031-25891-6_38

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  • DOI: https://doi.org/10.1007/978-3-031-25891-6_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25890-9

  • Online ISBN: 978-3-031-25891-6

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