Knowledge Representations in Technical Systems -- A Taxonomy

TL;DR

This paper provides a comprehensive taxonomy of knowledge representation techniques in artificial intelligence, focusing on their application in robotics to enhance human-centric task understanding and execution.

Contribution

It introduces a detailed taxonomy categorizing knowledge representations and demonstrates their application in robotics tasks, aiding the selection of suitable techniques.

Findings

Taxonomy facilitates selecting appropriate knowledge representations.

Applications demonstrate effectiveness in robotics tasks.

Provides insights into AI knowledge representation techniques.

Abstract

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.