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A Deep Learning Cognitive Architecture: Towards a Unified Theory of Cognition

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Intelligent Systems and Applications (IntelliSys 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1250))

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Abstract

This work suggests a novel approach to autonomous systems development linking autonomous technology to an integrated cognitive architecture with the aim of supporting a common artificial general intelligence (AGI) development. The paper provides a summary of strengths and weaknesses of some of the most known cognitive architecture and highlights how to support a generic artificial intelligent approach rather than ad hoc solutions. It also proposes objective evaluation criteria to assess a cognitive architecture. Finally, the proposed cognitive architecture is introduced: a Deep-Learning Artificial Neural Cognitive Architecture (D-LANCA), which aims to overcome current limits of cognitive frameworks for autonomous systems with the view to create a common artificial general intelligent (AGI) cognitive approach across industries.

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Authors and Affiliations

  1. Cranfield University, Cranfield, MK43 0AL, UK

    Isabella Panella, Luca Zanotti Fragonara & Antonios Tsourdos

Authors
  1. Isabella Panella
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  2. Luca Zanotti Fragonara
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  3. Antonios Tsourdos
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Corresponding author

Correspondence to Isabella Panella .

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Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Panella, I., Fragonara, L.Z., Tsourdos, A. (2021). A Deep Learning Cognitive Architecture: Towards a Unified Theory of Cognition. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1250. Springer, Cham. https://doi.org/10.1007/978-3-030-55180-3_42

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