DASH: Deception-Augmented Shared Mental Model for a Human-Machine Teaming System
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909972626931712 |
|---|---|
| author | Wan, Zelin Yoon, Han Jun Alluru, Nithin Moore, Terrence J. Nelson, Frederica F. Yoon, Seunghyun Lim, Hyuk Kim, Dan Dongseong Cho, Jin-Hee |
| author_facet | Wan, Zelin Yoon, Han Jun Alluru, Nithin Moore, Terrence J. Nelson, Frederica F. Yoon, Seunghyun Lim, Hyuk Kim, Dan Dongseong Cho, Jin-Hee |
| contents | We present DASH (Deception-Augmented Shared mental model for Human-machine teaming), a novel framework that enhances mission resilience by embedding proactive deception into Shared Mental Models (SMM). Designed for mission-critical applications such as surveillance and rescue, DASH introduces "bait tasks" to detect insider threats, e.g., compromised Unmanned Ground Vehicles (UGVs), AI agents, or human analysts, before they degrade team performance. Upon detection, tailored recovery mechanisms are activated, including UGV system reinstallation, AI model retraining, or human analyst replacement. In contrast to existing SMM approaches that neglect insider risks, DASH improves both coordination and security. Empirical evaluations across four schemes (DASH, SMM-only, no-SMM, and baseline) show that DASH sustains approximately 80% mission success under high attack rates, eight times higher than the baseline. This work contributes a practical human-AI teaming framework grounded in shared mental models, a deception-based strategy for insider threat detection, and empirical evidence of enhanced robustness under adversarial conditions. DASH establishes a foundation for secure, adaptive human-machine teaming in contested environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18616 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DASH: Deception-Augmented Shared Mental Model for a Human-Machine Teaming System Wan, Zelin Yoon, Han Jun Alluru, Nithin Moore, Terrence J. Nelson, Frederica F. Yoon, Seunghyun Lim, Hyuk Kim, Dan Dongseong Cho, Jin-Hee Human-Computer Interaction Artificial Intelligence Cryptography and Security Multiagent Systems We present DASH (Deception-Augmented Shared mental model for Human-machine teaming), a novel framework that enhances mission resilience by embedding proactive deception into Shared Mental Models (SMM). Designed for mission-critical applications such as surveillance and rescue, DASH introduces "bait tasks" to detect insider threats, e.g., compromised Unmanned Ground Vehicles (UGVs), AI agents, or human analysts, before they degrade team performance. Upon detection, tailored recovery mechanisms are activated, including UGV system reinstallation, AI model retraining, or human analyst replacement. In contrast to existing SMM approaches that neglect insider risks, DASH improves both coordination and security. Empirical evaluations across four schemes (DASH, SMM-only, no-SMM, and baseline) show that DASH sustains approximately 80% mission success under high attack rates, eight times higher than the baseline. This work contributes a practical human-AI teaming framework grounded in shared mental models, a deception-based strategy for insider threat detection, and empirical evidence of enhanced robustness under adversarial conditions. DASH establishes a foundation for secure, adaptive human-machine teaming in contested environments. |
| title | DASH: Deception-Augmented Shared Mental Model for a Human-Machine Teaming System |
| topic | Human-Computer Interaction Artificial Intelligence Cryptography and Security Multiagent Systems |
| url | https://arxiv.org/abs/2512.18616 |