Abstract
The area of agent systems design may be safely described as cluttered and disorganized, especially by those that situate themselves outside the “agent community”. Despite the wealth of bibliography on agent systems design and applications, there are few widely acknowledged design methods that have surfaced from testing and practice, mainly in laboratory settings. The paper contributes to the understanding of the field by presenting a critical review of methodologies that have emerged over the last few years to guide and explain agent systems design and development. The perspective for this review has been mainly formulated by posing important research questions in the field, and by attempting to interpret and discover latent hypotheses and underlying assumptions made by methodologies in reference to relevant research, both in agent systems and cooperative information systems practice and theory. The paper identifies significant challenges for agent systems methodologies that, if pursued, can contribute to a new understanding of the field that shifts the foci of current agent systems research, towards holistic design methods that place human users and information systems stakeholders at the centre of interest and involve them in the design process as much as possible.
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Notes
The AOR metamodel is not referred to as a methodology but it is directly relevant to work that can be included in terms of the purposes of this section.
BDI architectures consider that a rational agent having certain mental attitudes of Belief, Desire and Intention, representing, respectively, the information, motivational, and deliberative states of the agent (Georgeff et al. 1999).
Some information systems methodologies include guidelines and methods for feasibility studies concerned with economics like SSADM (Structured Systems Analysis and Design Methodology) (Weaver et al. 1998) and DSDM (Dynamic Systems Development Method) (The DSDM consortium, http://www.dsdm.org).
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Koutsabasis, P., Darzentas, J. Methodologies for agent systems development: underlying assumptions and implications for design. AI & Soc 23, 379–407 (2009). https://doi.org/10.1007/s00146-007-0110-9
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DOI: https://doi.org/10.1007/s00146-007-0110-9