Imperial College London
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ProfessorAlessandraRusso

Faculty of Engineering, Department of Computing

Professor in Applied Computational Logic
 
 
 
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Contact

 

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

 
 
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Location

 

560Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Bertino:2019:10.1109/icdcs.2019.00158,
author = {Bertino, E and White, G and Lobo, J and Ingham, J and Cirincione, GH and Russo, A and Law, M and Calo, S and Manotas, I and Verma, D and Jabal, AA and Cunnington, D and de, Mel G},
doi = {10.1109/icdcs.2019.00158},
publisher = {IEEE},
title = {Generative policies for coalition systems - a symbolic learning framework},
url = {http://dx.doi.org/10.1109/icdcs.2019.00158},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Policy systems are critical for managing missions and collaborative activities carried out by coalitions involving different organizations. Conventional policy-based management approaches are not suitable for next-generation coalitions that will involve not only humans, but also autonomous computing devices and systems. It is critical that those parties be able to generate and customize policies based on contexts and activities. This paper introduces a novel approach for the autonomic generation of policies by autonomous parties. The framework combines context free grammars, answer set programs, and inductionbased learning. It allows a party to generate its own policies, based on a grammar and some semantic constraints, by learning from examples. The paper also outlines initial experiments in the use of such a symbolic approach and outlines relevant research challenges, ranging from explainability to quality assessment of policies.
AU  - Bertino,E
AU  - White,G
AU  - Lobo,J
AU  - Ingham,J
AU  - Cirincione,GH
AU  - Russo,A
AU  - Law,M
AU  - Calo,S
AU  - Manotas,I
AU  - Verma,D
AU  - Jabal,AA
AU  - Cunnington,D
AU  - de,Mel G
DO  - 10.1109/icdcs.2019.00158
PB  - IEEE
PY  - 2019///
TI  - Generative policies for coalition systems - a symbolic learning framework
UR  - http://dx.doi.org/10.1109/icdcs.2019.00158
UR  - https://ieeexplore.ieee.org/document/8885292
UR  - http://hdl.handle.net/10044/1/77403
ER  -
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