A case-based reasoning based multi-agent cognitive map inference mechanism: An application to sales opportunity assessment

Namho Lee (), Jae Kwon Bae () and Chulmo Koo ()
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Namho Lee: Consulting Group, SAP Korea
Jae Kwon Bae: Dongyang University
Chulmo Koo: Chosun University

Information Systems Frontiers, 2012, vol. 14, issue 3, No 14, 653-668

Abstract: Abstract In order to propose a new cognitive map (CM) inference mechanism that does not require artificial assumptions, we developed a case-based reasoning (CBR) based mechanism called the CBRMCM (Case-Based Reasoning based Multi-agent Cognitive Map). The key idea of the CBRMCM mechanism involves converting all of the factors (nodes) that constitute the CM into intelligent agents that determine their own status by checking status changes and relationship with other agents and the results being reported to other related node agents. Furthermore, the CBRMCM is deployed when each node agent references the status of other related nodes to determine its own status value. This approach eliminates the artificial fuzzy value conversion and the numerical inference function that were required for obtaining CM inference. Using the CBRMCM mechanism, we have demonstrated that the task of analyzing a sales opportunity could be systematically and intelligently solved and thus, IS project managers can be provided with robust decision support.

Keywords: Cognitive Map (CM); Case-Based Reasoning (CBR); Case-Based Reasoning based Multi-agent Cognitive Map (CBRMCM); Sales opportunity assessment cases (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10796-011-9294-0

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