Conference item
Towards ILP-based LTLf passive learning
- Abstract:
- Inferring a LTLf formula from a set of example traces, also known as passive learning, is a challenging task for model-based techniques. Despite the combinatorial nature of the problem, current state-of-the-art solutions are based on exhaustive search. They use an example at the time to discard a single candidate formula at the time, instead of exploiting the full set of examples to prune the search space. This hinders their applicability when examples involve many atomic propositions or when the target formula is not small. This short paper proposes the first ILP-based approach for learning LTLf formula from a set of example traces, using a learning from answer sets system called ILASP. It compares it to both pure SAT-based techniques and the exhaustive search method. Preliminary experimental results show that our approach improves on previous SAT-based techniques and that has the potential to overcome the limitation of an exhaustive search by optimizing over the full set of examples. Further research directions for the ILP-based LTLf passive learning problem are also discussed.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 429.3KB, Terms of use)
-
- Publisher copy:
- 10.1007/978-3-031-49299-0_3
Authors
- Publisher:
- Springer Nature
- Host title:
- Inductive Logic Programming: 32nd International Conference, ILP 2023, Bari, Italy, November 13–15, 2023, Proceedings
- Pages:
- 30-45
- Series:
- Lecture Notes in Computer Science
- Series number:
- 14363
- Place of publication:
- Cham, Switzerland
- Publication date:
- 2023-12-22
- Acceptance date:
- 2023-08-25
- Event title:
- 32nd International Conference on Inductive Logic Programming (ILP 2023) @ IJCLR
- Event location:
- Bari, Italy
- Event website:
- https://ilp2023.unife.it/
- Event start date:
- 2023-11-13
- Event end date:
- 2023-11-15
- DOI:
- EISSN:
-
1611-3349
- ISSN:
-
0302-9743
- EISBN:
- 9783031492990
- ISBN:
- 9783031492983
- Language:
-
English
- Keywords:
- Pubs id:
-
1602896
- Local pid:
-
pubs:1602896
- Deposit date:
-
2024-04-14
Terms of use
- Copyright holder:
- Ielo et al.
- Copyright date:
- 2023
- Rights statement:
- © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from Springer Nature at https://dx.doi.org/10.1007/978-3-031-49299-0_3
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