Potoniec, Jędrzej2023-09-252023-09-252023Potoniec J., Are Quantified Boolean Formulas Hard for Reason-Able Embeddings?. W: Progress in Polish Artificial Intelligence Research 4, Wojciechowski A. (Ed.), Lipiński P. (Ed.)., Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, s. 343-348, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.54.978-83-66741-92-8http://hdl.handle.net/11652/4830https://doi.org/10.34658/9788366741928.54We aim to establish theoretical boundaries for the applicability of reason-able embeddings, a recently proposed method employing a transferable neural reasoner to shape a latent space of knowledge graph embeddings. Since reason-able embeddings rely on the ALC description logic, we construct a dataset of the hardest concepts in ALC by translating quantified boolean formulas (QBF) from QBFLIB, a benchmark for QBF solvers. We experimentally show the dataset is hard for a symbolic reasoner FaCT++, and analyze the results of reasoning with reason-able embeddings, concluding that the dataset is too hard for them.enDla wszystkich w zakresie dozwolonego użytkuFair use conditionartificial intelligenceneural-symbolic reasoningknowledge representationdescription logicssztuczna inteligencjawnioskowanie neuronalno-symbolicznereprezentacja wiedzylogika opisowaRozdział - monografiaLicencja PŁLUT License10.34658/9788366741928.54