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

arXiv: https://arxiv.org/abs/1911.07133 · HTML: https://mlsanity.com/paper/1911.07133/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2019.

## Built on by (most cited first, 8 of 8)

- [Data-driven Control of Agent-based Models: an Equation/Variable-free Machine Learning Approach](https://mlsanity.com/paper/2207.05779/) (2022)
- [Requirements Engineering for Machine Learning: A Review and Reflection](https://mlsanity.com/paper/2210.00859/) (2022)
- [Autoencoder-based Semantic Novelty Detection: Towards Dependable AI-based Systems](https://mlsanity.com/paper/2108.10851/) (2021)
- [Enhancing Deep Learning with Scenario-Based Override Rules: a Case Study](https://mlsanity.com/paper/2301.08114/) (2023)
- [A Reference Model for IoT Embodied Agents Controlled by Neural Networks](https://mlsanity.com/paper/2102.07589/) (2021)
- [A Measure for Level of Autonomy Based on Observable System Behavior](https://mlsanity.com/paper/2407.14975/) (2024)
- [On the Conditions for Domain Stability for Machine Learning: a Mathematical Approach](https://mlsanity.com/paper/2412.00464/) (2024)
- [Structural Alignment for Reliable Industrial AI: Bridging Physical Reality, Data, Models, and Human Intent](https://mlsanity.com/paper/2609.35400/) (2026)

## Abstract (arXiv)

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.