Agreeing on Role Adoption in Open Organisations
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Zusammenfassung
The organisational specification of a multi-agent system supports agents’ effectiveness in attaining their purpose, or prevent certain undesired behaviour from occurring. This requires that agents are able to find out about the organisational purpose and description and decide on its appropriateness for their own objectives. Organisational modeling languages are used to specify an agent system in terms of its roles, organizational structure, norms, etc. Agents take part in organisations by playing one or more of the specified roles for which they have the necessary capabilities.In this paper, we investigate the process of role adoption in the context of the well-known OperA organisational modelling language. In OperA, each organisation has a gatekeeper role responsible for admitting agents to the organisation. Agents playing the role of gatekeeper can interact with agents that want to enter the organisation in order to come to agreement on role adoption. That is, negotiate which roles they will play and under which conditions they will play them. This is possible by evaluating capability requirements for roles. We extend OperA to allow for the specification of role capabilities. This approach will be illustrated using the Blocks World for Teams (BW4T) domain.
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- BibTeX
Aldewereld, H., Dignum, V., Jonker, C. M. & Riemsdijk, M. B.,
(2012).
Agreeing on Role Adoption in Open Organisations.
KI - Künstliche Intelligenz: Vol. 26, No. 1.
Springer.
(S. 37-45).
DOI: 10.1007/s13218-011-0152-5
@article{mci/Aldewereld2012,
author = {Aldewereld, Huib AND Dignum, Virginia AND Jonker, Catholijn M. AND Riemsdijk, M. Birna},
title = {Agreeing on Role Adoption in Open Organisations},
journal = {KI - Künstliche Intelligenz},
volume = {26},
number = {1},
year = {2012},
,
pages = { 37-45 } ,
doi = { 10.1007/s13218-011-0152-5 }
}
author = {Aldewereld, Huib AND Dignum, Virginia AND Jonker, Catholijn M. AND Riemsdijk, M. Birna},
title = {Agreeing on Role Adoption in Open Organisations},
journal = {KI - Künstliche Intelligenz},
volume = {26},
number = {1},
year = {2012},
,
pages = { 37-45 } ,
doi = { 10.1007/s13218-011-0152-5 }
}
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Mehr Information
ISSN: 1610-1987
Datum: 2012
Typ: Text/Journal Article

