OBJECTIVE: One of the major topics towards robot consciousness is to give a robot the capabilities of self-consciousness. We propose that robot self-consciousness is based on higher order perception of the robot, in the sense that first-order robot perception is the immediate perception of the outer world, while higher order perception is the perception of the inner world of the robot. METHODS AND MATERIAL: We refer to a robot cognitive architecture that has been developed during almost 10 years at the RoboticsLab of the University of Palermo. The architecture is organized in three computational areas. The subconceptual area is concerned with the low level processing of perceptual data coming from the sensors. In the linguistic area, representation and processing are based on a logic formalism. In the conceptual area, the data coming from the subconceptual area are organized in conceptual categories. RESULTS: To model higher order perceptions in self-reflective agents, we introduce the notion of second-order points in conceptual space. Each point in this space corresponds to a self-reflective agent, i.e., the robot itself, persons, and other robots with introspective capabilities. CONCLUSIONS: The described model of robot self-consciousness, although effective, highlights open problems from the point of view of the computational requirements of the current state-of-art computer systems. Some future works that lets the robot to summarize its own past experiences should be investigated.
OBJECTIVE: One of the major topics towards robot consciousness is to give a robot the capabilities of self-consciousness. We propose that robot self-consciousness is based on higher order perception of the robot, in the sense that first-order robot perception is the immediate perception of the outer world, while higher order perception is the perception of the inner world of the robot. METHODS AND MATERIAL: We refer to a robot cognitive architecture that has been developed during almost 10 years at the RoboticsLab of the University of Palermo. The architecture is organized in three computational areas. The subconceptual area is concerned with the low level processing of perceptual data coming from the sensors. In the linguistic area, representation and processing are based on a logic formalism. In the conceptual area, the data coming from the subconceptual area are organized in conceptual categories. RESULTS: To model higher order perceptions in self-reflective agents, we introduce the notion of second-order points in conceptual space. Each point in this space corresponds to a self-reflective agent, i.e., the robot itself, persons, and other robots with introspective capabilities. CONCLUSIONS: The described model of robot self-consciousness, although effective, highlights open problems from the point of view of the computational requirements of the current state-of-art computer systems. Some future works that lets the robot to summarize its own past experiences should be investigated.