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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.12373 |
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| _version_ | 1866917624565202944 |
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| author | Valizadeh, Mojtaba Fijalkow, Nathanaël Berger, Martin |
| author_facet | Valizadeh, Mojtaba Fijalkow, Nathanaël Berger, Martin |
| contents | Linear temporal logic (LTL) is widely used in industrial verification. LTL formulae can be learned from traces. Scaling LTL formula learning is an open problem. We implement the first GPU-based LTL learner using a novel form of enumerative program synthesis. The learner is sound and complete. Our benchmarks indicate that it handles traces at least 2048 times more numerous, and on average at least 46 times faster than existing state-of-the-art learners. This is achieved with, among others, novel branch-free LTL semantics that has $O(\log n)$ time complexity, where $n$ is trace length, while previous implementations are $O(n^2)$ or worse (assuming bitwise boolean operations and shifts by powers of 2 have unit costs -- a realistic assumption on modern processors). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12373 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LTL learning on GPUs Valizadeh, Mojtaba Fijalkow, Nathanaël Berger, Martin Programming Languages Artificial Intelligence 68 D.3 Linear temporal logic (LTL) is widely used in industrial verification. LTL formulae can be learned from traces. Scaling LTL formula learning is an open problem. We implement the first GPU-based LTL learner using a novel form of enumerative program synthesis. The learner is sound and complete. Our benchmarks indicate that it handles traces at least 2048 times more numerous, and on average at least 46 times faster than existing state-of-the-art learners. This is achieved with, among others, novel branch-free LTL semantics that has $O(\log n)$ time complexity, where $n$ is trace length, while previous implementations are $O(n^2)$ or worse (assuming bitwise boolean operations and shifts by powers of 2 have unit costs -- a realistic assumption on modern processors). |
| title | LTL learning on GPUs |
| topic | Programming Languages Artificial Intelligence 68 D.3 |
| url | https://arxiv.org/abs/2402.12373 |