Obligation and Prohibition Norms Mining Algorithm for normative multi-agent systems

Moamin A. Mahmoud, Mohd Sharifuddin Ahmad, Azhana Ahmad, Mohd Zaliman Mohd Yusoff, Aida Mustapha, Nurzeatul Hamimah Abdul Hamid

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

14 Citations (Scopus)

Abstract

Currently, research in normative multi-agent systems focus on how a visitor or new agent detects and updates its host norms autonomously without being explicitly given by the host system. In this paper, we present our proposed algorithm to detect the obligation and prohibition norms which we called the Obligation and Prohibition Norms Mining algorithm (OPNM). The algorithm exploits the resources of the host system, implements data formatting, filtering, and extracting the exceptional events, i.e. those that entail rewards and penalties of the obligation and prohibition norms and identifies the ensuing normative protocol. In this work, we assume that an agent is aware of its environment and is able to reason about its surrounding events. We then demonstrate the operation of the algorithm by applying it on a typical scenario and analyzing the results.

Original languageEnglish
Title of host publicationAdvanced Methods and Technologies for Agent and Multi-Agent Systems
EditorsDariusz Barbucha, Le Manh Thanh, Robert Howlett, Lakhmi Jain
Pages115-124
Number of pages10
DOIs
Publication statusPublished - 01 Dec 2013

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume252
ISSN (Print)0922-6389

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

A. Mahmoud, M., Ahmad, M. S., Ahmad, A., Mohd Yusoff, M. Z., Mustapha, A., & Hamid, N. H. A. (2013). Obligation and Prohibition Norms Mining Algorithm for normative multi-agent systems. In D. Barbucha, L. Manh Thanh, R. Howlett, & L. Jain (Eds.), Advanced Methods and Technologies for Agent and Multi-Agent Systems (pp. 115-124). (Frontiers in Artificial Intelligence and Applications; Vol. 252). https://doi.org/10.3233/978-1-61499-254-7-115