FastLAS: scalable inductive logic programming incorporating domain-specific optimisation criteria

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• dc.contributor.author Law, Mark
• dc.contributor.author Russo, Alessandra
• dc.contributor.author Bertino, Elisa
• dc.contributor.author Broda, Krysia
• dc.contributor.author Lobo, Jorge
• dc.date.accessioned 2023-05-02T06:15:27Z
• dc.date.available 2023-05-02T06:15:27Z
• dc.date.issued 2020
• dc.description Comunicació presentada a The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), celebrat del 7 al 12 de febrer de 2020 a Nova York, Estats Units.
• dc.description.abstract Inductive Logic Programming (ILP) systems aim to find a set of logical rules, called a hypothesis, that explain a set of examples. In cases where many such hypotheses exist, ILP systems often bias towards shorter solutions, leading to highly general rules being learned. In some application domains like security and access control policies, this bias may not be desirable, as when data is sparse more specific rules that guarantee tighter security should be preferred. This paper presents a new general notion of a scoring function over hypotheses that allows a user to express domain-specific optimisation criteria. This is incorporated into a new ILP system, called FastLAS, that takes as input a learning task and a customised scoring function, and computes an optimal solution with respect to the given scoring function. We evaluate the accuracy of FastLAS over real-world datasets for access control policies and show that varying the scoring function allows a user to target domain-specific performance metrics. We also compare FastLAS to state-of-the-art ILP systems, using the standard ILP bias for shorter solutions, and demonstrate that FastLAS is significantly faster and more scalable.
• dc.description.sponsorship This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. Jorge Lobo was supported by the Spanish Ministry of Economy and Competitiveness under Grant Numbers TIN201681032P, MDM20150502, and the U.S. Army Research Office under agreement number W911NF1910432.
• dc.format.mimetype application/pdf
• dc.identifier.citation Law M, Russo A, Bertino E, Broda K, Lobo J. FastLAS: scalable inductive logic programming incorporating domain-specific optimisation criteria. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20); 2020 Feb 7-12; New York, USA. Palo Alto: AAAI; 2020. p. 2877-85. DOI: 10.1609/aaai.v34i03.5678
• dc.identifier.doi http://dx.doi.org/10.1609/aaai.v34i03.5678
• dc.identifier.isbn 978-1-57735-835-0
• dc.identifier.issn 2159-5399
• dc.identifier.uri http://hdl.handle.net/10230/56628
• dc.language.iso eng
• dc.publisher Association for the Advancement of Artificial Intelligence (AAAI)
• dc.relation.ispartof The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20); 2020 Feb 7-12; New York, USA. Palo Alto: AAAI; 2020. p. 2877-85.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2016-81032-P
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/MDM-2015-0502
• dc.rights © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org)
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.other Intel·ligència artificial
• dc.subject.other Programació lògica
• dc.title FastLAS: scalable inductive logic programming incorporating domain-specific optimisation criteria
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion