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

Law, Mark; Russo, Alessandra; Bertino, Elisa; Broda, Krysia; Lobo, Jorge

doi:http://dx.doi.org/10.1609/aaai.v34i03.5678

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

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

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.

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.