Formal Control for Uncertain Systems via Contract-Based Probabilistic Surrogates (Extended Version)
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912654937817088 |
|---|---|
| author | Schön, Oliver Haesaert, Sofie Soudjani, Sadegh |
| author_facet | Schön, Oliver Haesaert, Sofie Soudjani, Sadegh |
| contents | The requirement for identifying accurate system representations has not only been a challenge to fulfill, but it has compromised the scalability of formal methods, as the resulting models are often too complex for effective decision making with formal correctness and performance guarantees. Focusing on probabilistic simulation relations and surrogate models of stochastic systems, we propose an approach that significantly enhances the scalability and practical applicability of such simulation relations by eliminating the need to compute error bounds directly. As a result, we provide an abstraction-based technique that scales effectively to higher dimensions while addressing complex nonlinear agent-environment interactions with infinite-horizon temporal logic guarantees amidst uncertainty. Our approach trades scalability for conservatism favorably, as demonstrated on a complex high-dimensional vehicle intersection case study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16971 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Formal Control for Uncertain Systems via Contract-Based Probabilistic Surrogates (Extended Version) Schön, Oliver Haesaert, Sofie Soudjani, Sadegh Systems and Control Artificial Intelligence Multiagent Systems The requirement for identifying accurate system representations has not only been a challenge to fulfill, but it has compromised the scalability of formal methods, as the resulting models are often too complex for effective decision making with formal correctness and performance guarantees. Focusing on probabilistic simulation relations and surrogate models of stochastic systems, we propose an approach that significantly enhances the scalability and practical applicability of such simulation relations by eliminating the need to compute error bounds directly. As a result, we provide an abstraction-based technique that scales effectively to higher dimensions while addressing complex nonlinear agent-environment interactions with infinite-horizon temporal logic guarantees amidst uncertainty. Our approach trades scalability for conservatism favorably, as demonstrated on a complex high-dimensional vehicle intersection case study. |
| title | Formal Control for Uncertain Systems via Contract-Based Probabilistic Surrogates (Extended Version) |
| topic | Systems and Control Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2506.16971 |