Bernhard Rinner gave a lecture on “Self-awareness for autonomous systems” as part of the AI Excellence Lecture Series of the Artificial Intelligence Doctoral Academy.
Self-awareness is a broad concept borrowed from cognitive science and psychology that describes the property of a system, which has knowledge of “itself,” based on its own senses and internal models. This knowledge may take different forms, is based on perceptions of both internal and external phenomena, and is essential for being able to anticipate and adapt to unknown situations. Computational self-awareness methods comprise a new promising field that enables an autonomous agent to detect nonstationary conditions, to learn internal models of its environment, and to autonomously adapt its behavior and structure to the contextual tasks.
In this talk I will introduce the concept of computational self-awareness, explain its key capabilities and discuss the current state of research and open challenges.