A framework of awareness for artificial subjects

A small battery driven bio-patch, attached to the human body and monitoring various vital signals such as temperature, humidity, heart activity, muscle and brain activity, is an example of a highly resource constrained system, that has the demanding task to assess correctly the state of the monitored subject (healthy, normal, weak, ill, improving, worsening, etc.), and its own capabilities (attached to subject, working sensors, sufficient energy supply, etc.). These systems and many other systems would benefit from a sense of itself and its environment to improve robustness and sensibility of its behavior. Although we can get inspiration from fields like neuroscience, robotics, AI, and control theory, the tight resource and energy constraints imply that we have to understand accurately what technique leads to a particular feature of awareness, how it contributes to improved behavior, and how it can be implemented cost-efficiently in hardware or software. We review the concepts of environment- and self-models, semantic interpretation, semantic attribution, history, goals and expectations, prediction, and self-inspection, how they contribute to awareness and self-awareness, and how they contribute to improved robustness and sensibility of behavior.