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pro vyhledávání: '"Niskanen, Andreas"'
A major challenge in inductive logic programming is learning big rules. To address this challenge, we introduce an approach where we join small rules to learn big rules. We implement our approach in a constraint-driven system and use constraint solve
Externí odkaz:
http://arxiv.org/abs/2401.16215
Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experimen
Externí odkaz:
http://arxiv.org/abs/2308.09393
Autor:
Linsbichler, Thomas, Maratea, Marco, Niskanen, Andreas, Wallner, Johannes P., Woltran, Stefan
Publikováno v:
In Artificial Intelligence June 2022 307
Publikováno v:
In Artificial Intelligence June 2021 295
Akademický článek
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Boolean satisfiability (SAT) solvers allow for incremental computations, which is key to efficient employment of SAT solvers iteratively for developing complex decision and optimization procedures, including maximum satisfiability (MaxSAT) solvers. H
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a1151f41d9391f42aa8f584f9a0c9fdd
http://hdl.handle.net/10138/346992
http://hdl.handle.net/10138/346992
Recent advances in solvers for the Boolean satisfiability (SAT) based optimization paradigm of maximum satisfiability (MaxSAT) have turned MaxSAT into a viable approach to finding provably optimal solutions for various types of hard optimization prob
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1d28e6df668142f43628279e42509efd