CONAIM: A Conscious Attention-Based Integrated Model for Human-Like Robots

Abstract

Understanding consciousness is one of the most fascinating challenges of our time. From ancient civilizations to modern philosophers, questions have been asked on how one is conscious of his/her own existence and about the world that surrounds him/her. Although there is no precise definition for consciousness, there is an agreement that it is strongly related to human cognitive processes such as attention, a process capable of promoting a selection of a few stimuli from a huge amount of information that reaches us constantly. In order to bring the consciousness discussion to a computational scenario, this paper presents conscious attention-based integrated model (CONAIM), a formal model for machine consciousness based on an attentional schema for human-like agent cognition that integrates: short- and long-term memories, reasoning, planning, emotion, decision-making, learning, motivation, and volition. Experimental results in a mobile robotics domain show that the agent can attentively use motivation, volition, and memories to set its goals and learn new concepts and procedures based on exogenous and endogenous stimuli. By performing computation over an attentional space, the model also allowed the agent to learn over a much reduced state space. Further implementation under this model could potentially allow the agent to express sentience, self-awareness, self-consciousness, autonoetic consciousness, mineness, and perspectivalness.