A Bayesian approach to norm identification

Stephen Cranefield, Tony Savarimuthu, Felipe Meneguzzi, Nir Oren

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

When entering a system, an agent should be aware of the obligations and prohibitions (collectively norms) that will affect it. Several solutions to this norm identification problem have been proposed, which make use of observations of either other's norm compliant, or norm violating, behaviour. These solutions fail in situations where norms are typically violated, or complied with, respectively. In this paper we propose a Bayesian approach to norm identification which operates by learning from both norm compliant and norm violating behaviour. By utilising both types of behaviour, our work not only overcomes a major limitation of existing approaches, but also yields improved performance over the state-of-the-art. We evaluate its effectiveness empirically, showing, under certain conditions, high accuracy scores.

Original languageEnglish
Title of host publicationAAMAS 2015 - Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1743-1744
Number of pages2
Volume3
ISBN (Electronic)9781450337717
Publication statusPublished - 2015
Event14th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015 - Istanbul, Turkey
Duration: 4 May 20158 May 2015

Conference

Conference14th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015
CountryTurkey
CityIstanbul
Period4/05/158/05/15

Keywords

  • Bayesian reasoning
  • Norm identification
  • Norm recognition

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

Cite this

Cranefield, S., Savarimuthu, T., Meneguzzi, F., & Oren, N. (2015). A Bayesian approach to norm identification. In AAMAS 2015 - Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems (Vol. 3, pp. 1743-1744). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). http://www.ifaamas.org/Proceedings/aamas2015/aamas/p1743.pdf