{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:40:30Z","timestamp":1723016430385},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:p>Local search algorithms are well-known methods for solving large, hard instances of the satisfiability problem (SAT). The performance of these algorithms crucially depends on heuristics for setting noise parameters and scoring variables. The optimal setting for these heuristics varies for different instance distributions. In this paper, we present an approach for learning effective variable scoring functions and noise parameters by using reinforcement learning. We consider satisfiability problems from different instance distributions and learn specialized heuristics for each of them. Our experimental results show improvements with respect to both a WalkSAT baseline and another local search learned heuristic.<\/jats:p>","DOI":"10.24963\/kr.2023\/36","type":"proceedings-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T22:27:47Z","timestamp":1690842467000},"page":"365-373","source":"Crossref","is-referenced-by-count":0,"title":["Learning Interpretable Heuristics for WalkSAT"],"prefix":"10.24963","author":[{"given":"Yannet","family":"Interian","sequence":"first","affiliation":[{"name":"University of San Francisco"}]},{"given":"Sara","family":"Bernardini","sequence":"additional","affiliation":[{"name":"Royal Holloway University of London"}]}],"member":"10584","event":{"number":"20","sponsor":["Artificial Intelligence Journal","Principles of Knowledge Representation and Reasoning Inc.","Academic College of Tel-Aviv","European Association for Artificial Intelligence","National Science Foundation"],"acronym":"KR-2023","name":"20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}","start":{"date-parts":[[2023,9,2]]},"theme":"Artificial Intelligence","location":"Rhodes, Greece","end":{"date-parts":[[2023,9,8]]}},"container-title":["Proceedings of the Twentieth International Conference on Principles of Knowledge Representation and Reasoning"],"original-title":[],"deposited":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T22:28:21Z","timestamp":1690842501000},"score":1,"resource":{"primary":{"URL":"https:\/\/proceedings.kr.org\/2023\/36"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/kr.2023\/36","relation":{},"subject":[],"published":{"date-parts":[[2023,9]]}}}