Model Learning for Adjusting the Level of Automation in HCPS
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915625672114176 |
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| author | Hajnorouzi, Mehrnoush Rakow, Astrid Fränzle, Martin |
| author_facet | Hajnorouzi, Mehrnoush Rakow, Astrid Fränzle, Martin |
| contents | The steadily increasing level of automation in human-centred systems demands rigorous design methods for analysing and controlling interactions between humans and automated components, especially in safety-critical applications. The variability of human behaviour poses particular challenges for formal verification and synthesis. We present a model-based framework that enables design-time exploration of safe shared-control strategies in human-automation systems. The approach combines active automata learning -- to derive coarse, finite-state abstractions of human behaviour from simulations -- with game-theoretic reactive synthesis to determine whether a controller can guarantee safety when interacting with these models. If no such strategy exists, the framework supports iterative refinement of the human model or adjustment of the automation's controllable actions. A driving case study, integrating automata learning with reactive synthesis in UPPAAL, illustrates the applicability of the framework on a simplified driving scenario and its potential for analysing shared-control strategies in human-centred cyber-physical systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14437 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model Learning for Adjusting the Level of Automation in HCPS Hajnorouzi, Mehrnoush Rakow, Astrid Fränzle, Martin Human-Computer Interaction Formal Languages and Automata Theory The steadily increasing level of automation in human-centred systems demands rigorous design methods for analysing and controlling interactions between humans and automated components, especially in safety-critical applications. The variability of human behaviour poses particular challenges for formal verification and synthesis. We present a model-based framework that enables design-time exploration of safe shared-control strategies in human-automation systems. The approach combines active automata learning -- to derive coarse, finite-state abstractions of human behaviour from simulations -- with game-theoretic reactive synthesis to determine whether a controller can guarantee safety when interacting with these models. If no such strategy exists, the framework supports iterative refinement of the human model or adjustment of the automation's controllable actions. A driving case study, integrating automata learning with reactive synthesis in UPPAAL, illustrates the applicability of the framework on a simplified driving scenario and its potential for analysing shared-control strategies in human-centred cyber-physical systems. |
| title | Model Learning for Adjusting the Level of Automation in HCPS |
| topic | Human-Computer Interaction Formal Languages and Automata Theory |
| url | https://arxiv.org/abs/2511.14437 |