{"date_updated":"2023-09-06T08:27:33Z","ddc":["000"],"status":"public","publication_status":"published","page":"390-414","intvolume":"     13964","scopus_import":"1","has_accepted_license":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","conference":{"location":"Paris, France","name":"CAV: Computer Aided Verification","end_date":"2023-07-22","start_date":"2023-07-17"},"doi":"10.1007/978-3-031-37706-8_20","alternative_title":["LNCS"],"_id":"14259","type":"conference","volume":13964,"acknowledgement":"This research was funded in part by the German Research Foundation (DFG) project 427755713 Group-By Objectives in Probabilistic Verification (GOPro).","quality_controlled":"1","title":"Guessing winning policies in LTL synthesis by semantic learning","article_processing_charge":"Yes (in subscription journal)","date_published":"2023-07-17T00:00:00Z","month":"07","department":[{"_id":"KrCh"}],"year":"2023","language":[{"iso":"eng"}],"author":[{"id":"44CEF464-F248-11E8-B48F-1D18A9856A87","last_name":"Kretinsky","full_name":"Kretinsky, Jan","orcid":"0000-0002-8122-2881","first_name":"Jan"},{"id":"b21b0c15-30a2-11eb-80dc-f13ca25802e1","orcid":"0000-0002-1712-2165","first_name":"Tobias","full_name":"Meggendorfer, Tobias","last_name":"Meggendorfer"},{"last_name":"Prokop","full_name":"Prokop, Maximilian","first_name":"Maximilian"},{"first_name":"Sabine","full_name":"Rieder, Sabine","last_name":"Rieder"}],"day":"17","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"publisher":"Springer Nature","file":[{"file_id":"14276","date_created":"2023-09-06T08:25:50Z","access_level":"open_access","success":1,"file_size":428354,"creator":"dernst","checksum":"ed66278b61bb869e1baba3d9b9081271","content_type":"application/pdf","file_name":"2023_LNCS_CAV_Kretinsky.pdf","date_updated":"2023-09-06T08:25:50Z","relation":"main_file"}],"oa":1,"file_date_updated":"2023-09-06T08:25:50Z","date_created":"2023-09-03T22:01:16Z","abstract":[{"text":"We provide a learning-based technique for guessing a winning strategy in a parity game originating from an LTL synthesis problem. A cheaply obtained guess can be useful in several applications. Not only can the guessed strategy be applied as best-effort in cases where the game’s huge size prohibits rigorous approaches, but it can also increase the scalability of rigorous LTL synthesis in several ways. Firstly, checking whether a guessed strategy is winning is easier than constructing one. Secondly, even if the guess is wrong in some places, it can be fixed by strategy iteration faster than constructing one from scratch. Thirdly, the guess can be used in on-the-fly approaches to prioritize exploration in the most fruitful directions.\r\nIn contrast to previous works, we (i) reflect the highly structured logical information in game’s states, the so-called semantic labelling, coming from the recent LTL-to-automata translations, and (ii) learn to reflect it properly by learning from previously solved games, bringing the solving process closer to human-like reasoning.","lang":"eng"}],"oa_version":"Published Version","publication":"35th International Conference on Computer Aided Verification ","citation":{"ieee":"J. Kretinsky, T. Meggendorfer, M. Prokop, and S. Rieder, “Guessing winning policies in LTL synthesis by semantic learning,” in <i>35th International Conference on Computer Aided Verification </i>, Paris, France, 2023, vol. 13964, pp. 390–414.","mla":"Kretinsky, Jan, et al. “Guessing Winning Policies in LTL Synthesis by Semantic Learning.” <i>35th International Conference on Computer Aided Verification </i>, vol. 13964, Springer Nature, 2023, pp. 390–414, doi:<a href=\"https://doi.org/10.1007/978-3-031-37706-8_20\">10.1007/978-3-031-37706-8_20</a>.","ama":"Kretinsky J, Meggendorfer T, Prokop M, Rieder S. Guessing winning policies in LTL synthesis by semantic learning. In: <i>35th International Conference on Computer Aided Verification </i>. Vol 13964. Springer Nature; 2023:390-414. doi:<a href=\"https://doi.org/10.1007/978-3-031-37706-8_20\">10.1007/978-3-031-37706-8_20</a>","ista":"Kretinsky J, Meggendorfer T, Prokop M, Rieder S. 2023. Guessing winning policies in LTL synthesis by semantic learning. 35th International Conference on Computer Aided Verification . CAV: Computer Aided Verification, LNCS, vol. 13964, 390–414.","apa":"Kretinsky, J., Meggendorfer, T., Prokop, M., &#38; Rieder, S. (2023). Guessing winning policies in LTL synthesis by semantic learning. In <i>35th International Conference on Computer Aided Verification </i> (Vol. 13964, pp. 390–414). Paris, France: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-031-37706-8_20\">https://doi.org/10.1007/978-3-031-37706-8_20</a>","chicago":"Kretinsky, Jan, Tobias Meggendorfer, Maximilian Prokop, and Sabine Rieder. “Guessing Winning Policies in LTL Synthesis by Semantic Learning.” In <i>35th International Conference on Computer Aided Verification </i>, 13964:390–414. Springer Nature, 2023. <a href=\"https://doi.org/10.1007/978-3-031-37706-8_20\">https://doi.org/10.1007/978-3-031-37706-8_20</a>.","short":"J. Kretinsky, T. Meggendorfer, M. Prokop, S. Rieder, in:, 35th International Conference on Computer Aided Verification , Springer Nature, 2023, pp. 390–414."},"publication_identifier":{"issn":["0302-9743"],"eissn":["1611-3349"],"isbn":["9783031377051"]}}