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 about 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 linked as a complex system. One of the key processes related to consciousness is attention, a cognitive process capable of promoting a selection of just a few stimuli among a huge number of information that reaches us constantly. In order to bring the consciousness discussion to a computational scenario, previous works presented CONAIM (Conscious Attention-based Integrated Model), 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 under a mobile robotics domain suggest that the agent can demonstrate awareness, sentience, self-awareness, self-consciousness, auto noetic consciousness, fineness, volition and perspectivalness based on exogenous and endogenous stimuli. By performing computation over an attentional space, the model also allowed the agent to learn over a 95% reduced space-state. This work proposes the development and computational implementation of the semantic memory of the CONAIM model.
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