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Optimal environmental simulation settings to observe exceptional events in social agent societies

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dc.citedby1
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorAhmad M.S.en_US
dc.contributor.authorAhmad A.en_US
dc.contributor.authorMustapha A.en_US
dc.contributor.authorYusoff M.Z.M.en_US
dc.contributor.authorHamid N.H.A.en_US
dc.contributor.authorid55247787300en_US
dc.contributor.authorid56036880900en_US
dc.contributor.authorid55390963300en_US
dc.contributor.authorid57200530694en_US
dc.contributor.authorid22636590200en_US
dc.contributor.authorid55419558500en_US
dc.date.accessioned2023-12-29T07:43:53Z
dc.date.available2023-12-29T07:43:53Z
dc.date.issued2013
dc.description.abstractSocial norms learning in agent societies through reward or penalty observations have become the subject of interest in many studies. However, very few studies have examined the optimal environmental settings that would allow agents to learn through such observations effectively. This study presents a combination of environmental simulation parameters to discover the optimal settings for observing reward or penalty events, which are called the exceptional events, within a social agent group. The environmental settings consist of several variables which are the cycle time, observation limit of detector agent, domain size, population density of domain agents and occurrence of reward or penalty (exceptional) events in the domain. The value of each variable is arbitrarily set to low, medium or high. To implement the simulation, a virtual environment has been created with the variables settings to examine different situations. Within the steps of the tests, some cases are excluded because they do not significantly contribute to optimal environment for social learning. The results of the tests show that each variable has different effect on the environment and that a variable that has a strong positive effect does not individually offer the optimal solution. However, combining variables that have strong positive effects could offer optimal solutions. Briefly, the study aims to examine and identify the effect of some environmental variables on observation process of exceptional events and suggests the optimal settings to learn through observation. � 2013 Asian Network for Scientific Information.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.3923/jai.2013.191.209
dc.identifier.epage209
dc.identifier.issue3
dc.identifier.scopus2-s2.0-84887528337
dc.identifier.spage191
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84887528337&doi=10.3923%2fjai.2013.191.209&partnerID=40&md5=dcefbf7f99d5b7aa2b4576efd1914cb6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29990
dc.identifier.volume6
dc.pagecount18
dc.sourceScopus
dc.sourcetitleJournal of Artificial Intelligence
dc.subjectExceptional events observation
dc.subjectIntelligent software agent
dc.subjectNormative system
dc.subjectSimulation model
dc.titleOptimal environmental simulation settings to observe exceptional events in social agent societiesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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