Zobrazeno 1 - 10
of 518
pro vyhledávání: '"Luc De Raedt"'
Publikováno v:
Frontiers in Robotics and AI, Vol 7 (2020)
Robotic agents should be able to learn from sub-symbolic sensor data and, at the same time, be able to reason about objects and communicate with humans on a symbolic level. This raises the question of how to overcome the gap between symbolic and sub-
Externí odkaz:
https://doaj.org/article/ba4a3fbdbc1645c886551b8ce23e8334
Relational autocompletion is the problem of automatically filling out some missing values in multi-relational data. We tackle this problem within the probabilistic logic programming framework of Distributional Clauses (DC), which supports both discre
Externí odkaz:
http://arxiv.org/abs/2001.08603
Publikováno v:
Theory and Practice of Logic Programming. :1-50
Argumentation problems are concerned with determining the acceptability of a set of arguments from their relational structure. When the available information is uncertain, probabilistic argumentation frameworks provide modeling tools to account for i
Publikováno v:
Journal of Artificial Intelligence Research. 76:1-58
Combinatorics math problems are often used as a benchmark to test human cognitive and logical problem-solving skills. These problems are concerned with counting the number of solutions that exist in a specific scenario that is sketched in natural lan
Publikováno v:
Theory and Practice of Logic Programming. 22:770-775
Publikováno v:
Bioinformatics. 38:3245-3251
Motivation Network-based driver identification methods that can exploit mutual exclusivity typically fail to detect rare drivers because of their statistical rigor. Propagation-based methods in contrast allow recovering rare driver genes, but the int
Publikováno v:
Electronic Proceedings in Theoretical Computer Science. 364:126-128
Publikováno v:
Electronic Proceedings in Theoretical Computer Science. 364:153-155
Publikováno v:
Heyninck, J L A, Kern-Isberner, G & Meyer, T 2022, Lexicographic Entailment, Syntax Splitting and the Drowning Problem . in Luc De Raedt (ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence . International Joint Conferences on Artificial Intelligence, pp. 2662-2668, The Thirty-First International Joint Conference on Artificial Intelligence, Vienna, Austria, 23/07/22 . https://doi.org/10.24963/ijcai.2022/369
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2662-2668
STARTPAGE=2662;ENDPAGE=2668;TITLE=Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2662-2668
STARTPAGE=2662;ENDPAGE=2668;TITLE=Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Lexicographic inference is a well-known and popular approach to reasoning with non-monotonic conditionals. It is a logic of very high-quality, as it extends rational closure and avoids the so-called drowning problem. It seems, however, this high qual
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c4dfee7f31e6949d5ac92dbb4e4e5848
https://hdl.handle.net/1820/c53acd6f-2dad-4add-a863-d19e34ea5b92
https://hdl.handle.net/1820/c53acd6f-2dad-4add-a863-d19e34ea5b92
An issue that has so far received only limited attention in probabilistic logic programming (PLP) is the modeling of so-called epistemic uncertainty, the uncertainty about the model itself. Accurately quantifying this model uncertainty is paramount t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb98c194c304b268a635dbe50f8553dd
https://lirias.kuleuven.be/handle/20.500.12942/691670
https://lirias.kuleuven.be/handle/20.500.12942/691670