Contextual categorization: A mechanism linking perception and knowledge in modeling and simulating perceived events as actions

Abstract

The specific objective of this paper is to introduce the computer model ACACIA (Action by Contextually Automated Categorizing Interactive Agents) capable of simulating the way in which context is taken into account for the interpretation of perceived actions elaborated by a number of autonomous moving agents in a bidimensional space. With this in mind, we will examine some different modeling approaches in Artificial Intelligence and Artificial Life and emphasize the strong and weak points of each approach in relation to the set of issues addressed by our theory based on Contextual Categorization. Second, we provide a theoretical explanation of how contextual categorization accounts for temporal and environmental context to interpret ongoing situations in terms of perceived action. Finally, we describe the computer implementation of ACACIA, and we propose a preliminary simulation of a simple situation using StarLogo software.