Autonomics: In Search of a Foundation for Next Generation Autonomous Systems

TL;DR

This paper emphasizes the need for a foundational, community-driven framework for developing trustworthy next-generation autonomous systems, focusing on decision-making, simulation, and integration of model-driven and machine learning techniques.

Contribution

It identifies key challenges in specifying, simulating, and engineering autonomous systems, proposing autonomics as a new research area to address these issues.

Findings

Highlights the importance of a community-controlled foundation for autonomous systems

Identifies critical challenges in decision-making and simulation for autonomous systems

Proposes autonomics as a new research field to advance system engineering

Abstract

The potential benefits of autonomous systems have been driving intensive development of such systems, and of supporting tools and methodologies. However, there are still major issues to be dealt with before such development becomes commonplace engineering practice, with accepted and trustworthy deliverables. We argue that a solid, evolving, publicly available, community-controlled foundation for developing next generation autonomous systems is a must. We discuss what is needed for such a foundation, identify a central aspect thereof, namely, decision-making, and focus on three main challenges: (i) how to specify autonomous system behavior and the associated decisions in the face of unpredictability of future events and conditions and the inadequacy of current languages for describing these; (ii) how to carry out faithful simulation and analysis of system behavior with respect to rich environments that include humans, physical artifacts, and other systems,; and (iii) how to engineer systems that combine executable model-driven techniques and data-driven machine learning techniques. We argue that autonomics, i.e., the study of unique challenges presented by next generation autonomous systems, and research towards resolving them, can introduce substantial contributions and innovations in system engineering and computer science.