Knowledge Representations in Technical Systems -- A Taxonomy
This work addresses the problem of knowledge representation for robotics and AI systems, but it is incremental as it primarily categorizes existing techniques rather than introducing new ones.
The paper tackles the challenge of enabling technical systems like robots to understand and perform human-desired tasks by providing a taxonomy of knowledge representation techniques and their applications in robotics, aiming to facilitate the selection of appropriate methods for specific problems.
The recent usage of technical systems in human-centric environments leads to the question, how to teach technical systems, e.g., robots, to understand, learn, and perform tasks desired by the human. Therefore, an accurate representation of knowledge is essential for the system to work as expected. This article mainly gives insight into different knowledge representation techniques and their categorization into various problem domains in artificial intelligence. Additionally, applications of presented knowledge representations are introduced in everyday robotics tasks. By means of the provided taxonomy, the search for a proper knowledge representation technique regarding a specific problem should be facilitated.