Abstract
When entering a system, an agent should be aware of the obligations and prohibitions (collectively norms) that affect it. Existing solutions to this norm identification problem make use of observations of either norm compliant, or norm violating, behaviour. Thus, they assume an extreme situation where norms are typically violated, or complied with. In this paper we propose a Bayesian approach to norm identification which operates by learning from both
norm compliant and norm violating behaviour. We evaluate our approach’s
effectiveness empirically and compare its accuracy to existing approaches. By utilising both types of behaviour, we not only overcome a major limitation of such approaches, but also obtain improved performance over the state of the art, allowing norms to be learned with fewer observations.
norm compliant and norm violating behaviour. We evaluate our approach’s
effectiveness empirically and compare its accuracy to existing approaches. By utilising both types of behaviour, we not only overcome a major limitation of such approaches, but also obtain improved performance over the state of the art, allowing norms to be learned with fewer observations.
Original language | English |
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Title of host publication | ECAI 2016 |
Editors | Gal A. Kaminka, Maria Fox, Paolo Bouquet, Eyke Hüllermeier, Virginia Dignum, Frank van Harmelen |
Publisher | IOS Press |
Pages | 622 - 629 |
Number of pages | 8 |
Volume | 285 |
ISBN (Electronic) | 978-1-61499-672-9 |
ISBN (Print) | 978-1-61499-671-2 |
DOIs | |
Publication status | Published - 30 Sept 2016 |
Publication series
Name | Frontiers in Artificial Intelligence and Applications |
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Publisher | IOS Press |
ISSN (Print) | 0922-6389 |
ISSN (Electronic) | 1879-8314 |