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.
Highlights
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. 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
Validation goes beyond the execution and ensures that the developed system corresponds to the ideal or desired behavior
Validation process Bringing V&V into the methodology of development of cognitive architectures/models, we can say that verification is primarily related to the functional design and implementation stages, while validation is especially important to determine if the overall design meets the main objective of achieving human-like behavior
Summary
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. Cognitive architectures have evolved over the past 50 years to become a solid option for the representation and modeling of intelligent behaviors searching to emulate natural human behavior, which in turn allows to provide synthetic agents with multi-level reasoning capabilities Achieving this natural behavior implies the development of human cognitive functions in the CAs such as memory, attention, planning, and decision-making, through the study, structuration, and integration of domains in the field of cognitive sciences (psychology, philosophy, biology, etc.)
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