{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T07:00:59Z","timestamp":1762326059446,"version":"build-2065373602"},"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":[[2025,11]]},"abstract":"<jats:p>Model counting (also known as #SAT) is a fundamental\n\nproblem in knowledge representation and reasoning, with\n\napplications ranging from probabilistic inference to formal\n\nverification. However, state-of-the-art model counters are\n\nlimited by computational resources on a single machine. In\n\nthis paper, we propose a novel distributed framework for\n\nmodel counting, exploiting the embarrassingly parallel\n\nnature of the problem. By decomposing the search space into\n\nindependent subproblems and distributing them across\n\ndifferent computation nodes, our approach achieves\n\nnear-linear scalability on practical instances. Extensive\n\nexperiments on standard benchmarks demonstrate both the\n\neffectiveness and efficiency of our framework.<\/jats:p>","DOI":"10.24963\/kr.2025\/65","type":"proceedings-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:10:44Z","timestamp":1762323044000},"page":"670-681","source":"Crossref","is-referenced-by-count":0,"title":["An Embarrassingly Parallel Model Counter"],"prefix":"10.24963","author":[{"given":"Zhenghang","family":"Xu","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Northeast Normal University, Changchun, China"},{"name":"Key Laboratory of Applied Statistics of MOE, Northeast Normal University, Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minghao","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northeast Normal University, Changchun, China"},{"name":"Key Laboratory of Applied Statistics of MOE, Northeast Normal University, Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean Marie","family":"Lagniez","sequence":"additional","affiliation":[{"name":"Univ. Artois, CNRS, CRIL, F-62300 Lens, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"22nd International Conference on Principles of Knowledge Representation and Reasoning {KR-2025}","theme":"Artificial Intelligence","location":"Melbourne, Australia","acronym":"KR-2025","number":"22","sponsor":["Artificial Intelligence Journal","Principles of Knowledge Representation and Reasoning Inc.","Academic College of Tel-Aviv","European Association for Artificial Intelligence","National Science Foundation"],"start":{"date-parts":[[2025,11,11]]},"end":{"date-parts":[[2025,11,17]]}},"container-title":["Proceedings of the TwentySecond International Conference on Principles of Knowledge Representation and Reasoning"],"original-title":[],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:11:19Z","timestamp":1762323079000},"score":1,"resource":{"primary":{"URL":"https:\/\/proceedings.kr.org\/2025\/65"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/kr.2025\/65","relation":{},"subject":[],"published":{"date-parts":[[2025,11]]}}}