Methodological aspects for cognitive architectures construction: a study and proposal
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Methodological aspects for cognitive architectures construction: a study and proposal Juan P. Jiménez1 · Luis Martin1 · Iván Axel Dounce1 · Cynthia Ávila‑Contreras1 · Félix Ramos1
© The Author(s) 2020
Abstract In the field of Artificial Intelligence (AI), efforts to achieve human-like behavior have taken very different paths through time. Cognitive Architectures (CAs) differentiate from traditional AI approaches, due to their intention to model cognitive and behavioral processes by understanding the brain’s structure and their functionalities in a natural way. However, the development of distinct CAs has not been easy, mainly because there is no consensus on the theoretical basis, assumptions or even purposes for their creation nor how well they reflect human function. In consequence, there is limited information about the methodological aspects to construct this type of models. To address this issue, some initial statements are established to contextualize about the origins and directions of cognitive architectures and their development, which help to outline perspectives, approaches and objectives of this work, supported by a brief study of methodological strategies and historical aspects taken by some of the most relevant architectures to propose a methodology which covers general perspectives for the construction of CAs. This proposal is intended to be flexible, focused on use-case tasks, but also directed by theoretic paradigms or manifestos. A case study between cognitive functions is then detailed, using visual perception and working memory to exemplify the proposal’s assumptions, postulates and binding tools, from their meta-architectural conceptions to validation. Finally, the discussion addresses the challenges found at this stage of development and future work directions. Keywords Methodology · Bio-inspired cognitive architectures · Cognition · Cognitive sciences · Neurosciences
* Félix Ramos [email protected] Juan P. Jiménez [email protected] Luis Martin [email protected] Iván Axel Dounce [email protected] 1
Nature Inspired Computing Lab, Cinvestav I.P.N. Unidad Guadalajara, Av. del Bosque 1145, Col. El bajío, 45019 Guadalajara, JAL, Mexico
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1 Introduction In recent years, the field of Artificial Intelligence (AI) has developed several study branches for generation, representation, analysis, and gathering of cognition and behavior of intelligent computational agents. Although it could be supposed that artificial intelligence -as an effort to model brain function- follows a relatively homogenous abstraction path to emulate human mind, some of the study areas of AI have opted for traditional learning and modeling approaches. These are often supported by knowledge-based systems, learning classifiers, neural networks, deep learning, statistical learning and other similar learning-based methods (Arbib 2003, 2007); while some others, which use holistic cognitive approaches, non-computational or mixed scopes, are foc
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