On Modeling Tacit Knowledge for Intelligent Systems

Abstract

In an attempt to support efforts to narrow the gap between current Artificial Intelligence and actual intelligent human behavior, this paper addresses Tacit Knowledge. Tacit Knowledge is analyzed and separated into articulable and inarticulable for ease of scrutiny. Concepts and ideas are taken up from knowledge management literature aiming to understand the scope of knowledge. Among the bailed out concepts “particulars” and “concepts” stand out, and “preconcept” is suggested as an intermediate phase between the former two. These concepts are placed into mental processes of knowledge resulting in an alternative neurological model of knowledge acquisition. The model’s target is to provide a picture as detailed as possible of the processes executed by the brain to make learning achievable. It encompasses from sensing the stimuli that is produced by the environment that are collected by sensory receptors to turn them into electrical impulses that are transmitted to the brain to climax with the emergence of concepts, from which increasingly complex knowledge is built. The model is then expanded to the social level.