Cognitive architectural model for solving clustering problem

Abstract

Human in an environment sees and then perceives objects of interest before he/she tries to find the correlation and association between the various objects in the region of their interest (ROI). By doing so, the agent here, the human develops an understanding of the environment, may it be static and certain or dynamic and uncertain. This paper simulates such an ability of humans, vital to his/her understanding, after being exposed to a visual stimulus. Filtration or selective attention happens then followed by clustering based on identified associations. These clusters form the basis of understanding and stored as Concept maps inside the long-term memory. In order to simulate this feature, various techniques and clustering algorithms exist. This work is a cognitive architectural approach to tackle the clustering problem, as it is a more natural and intuitive approach followed by humans. The ACT-R architecture has been chosen for the task.