What you need to know about a learning robot: Identifying the enabling architecture of complex systems

TL;DR

This paper presents elsarticle.cls as an enabling architecture for consistent, clash-free Elsevier manuscript formatting, detailing its dependencies, options, and installation workflow. The main contributions include a thorough guide to the class features, frontmatter handling, theorem environments, and natbib integration, along with a comparative view against elsart.cls to highlight improved compatibility. The work facilitates reliable and scalable manuscript preparation by reducing package conflicts and providing flexible formatting options, thereby streamlining publication workflows for researchers. The practical impact is clearer, more maintainable documentation and easier adaptation to journal-specific styles, enhancing reproducibility and sharing of scientific results.

Abstract

Nowadays, we are dealing more and more with robots and AI in everyday life. However, their behavior is not always apparent to most lay users, especially in error situations. As a result, there can be misconceptions about the behavior of the technologies in use. This, in turn, can lead to misuse and rejection by users. Explanation, for example, through transparency, can address these misconceptions. However, it would be confusing and overwhelming for users if the entire software or hardware was explained. Therefore, this paper looks at the 'enabling' architecture. It describes those aspects of a robotic system that might need to be explained to enable someone to use the technology effectively. Furthermore, this paper is concerned with the 'explanandum', which is the corresponding misunderstanding or missing concepts of the enabling architecture that needs to be clarified. We have thus developed and present an approach for determining this 'enabling' architecture and the resulting 'explanandum' of complex technologies.