FastLAS: scalable inductive logic programming incorporating domain-specific optimisation criteria
File(s)
Author(s)
Law, Mark
Russo, Alessandra
Bertino, Elisa
Broda, Krysia
Lobo, Jorge
Type
Conference Paper
Abstract
Inductive Logic Programming (ILP) systems aim to find a setof logical rules, called a hypothesis, that explain a set of ex-amples. In cases where many such hypotheses exist, ILP sys-tems often bias towards shorter solutions, leading to highlygeneral rules being learned. In some application domains likesecurity and access control policies, this bias may not be de-sirable, as when data is sparse more specific rules that guaran-tee tighter security should be preferred. This paper presents anew general notion of ascoring functionover hypotheses thatallows a user to express domain-specific optimisation criteria.This is incorporated into a new ILP system, calledFastLAS,that takes as input a learning task and a customised scoringfunction, and computes an optimal solution with respect tothe given scoring function. We evaluate the accuracy of Fast-LAS over real-world datasets for access control policies andshow that varying the scoring function allows a user to tar-get domain-specific performance metrics. We also compareFastLAS to state-of-the-art ILP systems, using the standardILP bias for shorter solutions, and demonstrate that FastLASis significantly faster and more scalable.
Date Issued
2020-04-03
Date Acceptance
2019-11-10
Citation
2020, pp.2877-2885
Publisher
Association for the Advancement of ArtificialIntelligence
Start Page
2877
End Page
2885
Copyright Statement
© 2020, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
Intelligence (www.aaai.org). All rights reserved.
Sponsor
IBM United Kingdom Ltd
Identifier
https://ojs.aaai.org/index.php/AAAI/article/view/5678
Grant Number
4603317662
Source
The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020)
Start Date
2020-02-07
Finish Date
2020-02-12
Coverage Spatial
New York, USA
Date Publish Online
2020-04-03