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Bandit-based Monte-Carlo structure learning of probabilistic logic programs

, , and . Machine Learning, 100 (1): 127--156 (2015)

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A Comparison of MCMC Sampling for Probabilistic Logic Programming., , , and . AI*IA, volume 11946 of Lecture Notes in Computer Science, page 18-29. Springer, (2019)SLGAD Resolution for Inference on Logic Programs with Annotated Disjunctions.. Fundam. Informaticae, 102 (3-4): 429-466 (2010)Modeling Smart Contracts with Probabilistic Logic Programming., , and . BIS (Workshops), volume 394 of Lecture Notes in Business Information Processing, page 86-98. Springer, (2020)Bandit-based Monte-Carlo structure learning of probabilistic logic programs, , and . Machine Learning, 100 (1): 127--156 (2015)Exploiting Association and Correlation Rules - Parameters for Improving the K2 Algorithm., , and . ECAI, page 500-504. IOS Press, (2004)Semantics for Hybrid Probabilistic Logic Programs with Function Symbols: Technical Summary., , and . ICLP Workshops, volume 3193 of CEUR Workshop Proceedings, CEUR-WS.org, (2022)The Distribution Semantics is Well-Defined for All Normal Programs.. PLP@ICLP, volume 1413 of CEUR Workshop Proceedings, page 69-84. CEUR-WS.org, (2015)Learning Logic Programs with Annotated Disjunctions.. ILP, volume 3194 of Lecture Notes in Computer Science, page 270-287. Springer, (2004)A Distribution Semantics for Probabilistic Ontologies., , , and . URSW, volume 778 of CEUR Workshop Proceedings, page 75-86. CEUR-WS.org, (2011)An Iterative Fixpoint Semantics for MKNF Hybrid Knowledge Bases with Function Symbols., , , and . ICLP Technical Communications / Doctoral Consortium, volume 364 of EPTCS, page 65-78. (2022)