Imperial College London
Portrait Picture

ProfessorAlessandraRusso

Faculty of Engineering, Department of Computing

Professor in Applied Computational Logic
 
 
 
//

Contact

 

+44 (0)20 7594 8312a.russo Website

 
 
//

Location

 

560Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@inproceedings{White:2019:10.1109/ICMLA.2019.00214,
author = {White, G and Cunnington, D and Law, M and Bertino, E and De, Mel G and Russo, A},
doi = {10.1109/ICMLA.2019.00214},
pages = {1314--1321},
title = {A comparison between statistical and symbolic learning approaches for generative policy models},
url = {http://dx.doi.org/10.1109/ICMLA.2019.00214},
year = {2019}
}
Download

RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Generative Policy Models (GPMs) have been proposed as a method for future autonomous decision making in a distributed, collaborative environment. To learn a GPM, previous policy examples that contain policy features and the corresponding policy decisions are used. Recently, GPMs have been constructed using both symbolic and statistical learning algorithms. In either case, the goal of the learning process is to create a model across a wide range of contexts from which specific policies may be generated in a given context. Empirically, we expect each learning approach to provide certain advantages over the other. This paper assesses the relative performance of each learning approach in order to examine these advantages and disadvantages. Several carefully prepared data sets are used to train a variety of models across different learning algorithms, where models for each learning algorithm are trained with varying amounts of labelled examples. The performance of each model is evaluated across a variety of metrics which indicates the strength of each learning algorithm for the different scenarios presented and the amount of training data provided. Finally, future research directions are outlined to fully realise GPMs in a distributed, collaborative environment.
AU  - White,G
AU  - Cunnington,D
AU  - Law,M
AU  - Bertino,E
AU  - De,Mel G
AU  - Russo,A
DO  - 10.1109/ICMLA.2019.00214
EP  - 1321
PY  - 2019///
SP  - 1314
TI  - A comparison between statistical and symbolic learning approaches for generative policy models
UR  - http://dx.doi.org/10.1109/ICMLA.2019.00214
ER  -
Download