# Extending MAPE-K to support Human-Machine Teaming

arXiv: https://arxiv.org/abs/2203.13036 · HTML: https://mlsanity.com/paper/2203.13036/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2022.

## Built on by (most cited first, 7 of 7)

- [Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems](https://mlsanity.com/paper/2307.06187/) (2023)
- [NOMAD: A Natural, Occluded, Multi-scale Aerial Dataset, for Emergency Response Scenarios](https://mlsanity.com/paper/2309.09518/) (2023)
- [Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms](https://mlsanity.com/paper/2505.23576/) (2025)
- [Psych-Occlusion: Using Visual Psychophysics for Aerial Detection of Occluded Persons during Search and Rescue](https://mlsanity.com/paper/2412.05553/) (2024)
- [Advancing Human-Machine Teaming: Concepts, Challenges, and Applications](https://mlsanity.com/paper/2503.16518/) (2025)
- [Autonomic Microservice Management via Agentic AI and MAPE-K Integration](https://mlsanity.com/paper/2506.22185/) (2025)
- [KYA: A Framework-Agnostic Trust Layer for Autonomous Systems with Verifiable Provenance and Hierarchical Policy Composition](https://mlsanity.com/paper/2605.25376/) (2026)

## Abstract (arXiv)

The MAPE-K feedback loop has been established as the primary reference model for self-adaptive and autonomous systems in domains such as autonomous driving, robotics, and Cyber-Physical Systems. At the same time, the Human Machine Teaming (HMT) paradigm is designed to promote partnerships between humans and autonomous machines. It goes far beyond the degree of collaboration expected in human-on-the-loop and human-in-the-loop systems and emphasizes interactions, partnership, and teamwork between humans and machines. However, while MAPE-K enables fully autonomous behavior, it does not explicitly address the interactions between humans and machines as intended by HMT. In this paper, we present the MAPE-K-HMT framework which augments the traditional MAPE-K loop with support for HMT. We identify critical human-machine teaming factors and describe the infrastructure needed across the various phases of the MAPE-K loop in order to effectively support HMT. This includes runtime models that are constructed and populated dynamically across monitoring, analysis, planning, and execution phases to support human-machine partnerships. We illustrate MAPE-K-HMT using examples from an autonomous multi-UAV emergency response system, and present guidelines for integrating HMT into MAPE-K.