A Bayesian approach to norm identification

Stephen Cranefield, Felipe Meneguzzi, Nir Oren, Bastin Tony Roy Savarimuthu

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

14 Citations (Scopus)
15 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationECAI 2016
EditorsGal A. Kaminka, Maria Fox, Paolo Bouquet, Eyke Hüllermeier, Virginia Dignum, Frank van Harmelen
PublisherIOS Press
Pages622 - 629
Number of pages8
ISBN (Electronic)978-1-61499-672-9
ISBN (Print)978-1-61499-671-2
Publication statusPublished - 30 Sept 2016

Publication series

NameFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Bibliographical note

F. Meneguzzi thanks Conselho Nacional de Desenvolvimento Cient´ıfico e Tecnologico (CNPq) through the Universal Grant (Grant ´ref. 482156/2013-9) and PQ fellowship (Grant ref. 306864/2013-4).

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