A case-based reasoning based multi-agent cognitive map inference mechanism: An application to sales opportunity assessment
- 263 Downloads
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.
KeywordsCognitive Map (CM) Case-Based Reasoning (CBR) Case-Based Reasoning based Multi-agent Cognitive Map (CBRMCM) Sales opportunity assessment cases
- Lopez, B. (1993). Reactive planning through the integration of a case-based system and a rule-based system. In Sloman et al. (Eds.), Prospects for artificial intelligence (pp. 189–198). IOS Press.Google Scholar
- Pews, G., & Wess, S. (1993). Combining case-based and model-based approaches for diagnostic applications in technical domains. In Proc. of First European Workshop on Case-Based Reasoning. 2, pp. 325–328.Google Scholar
- Plaza, E., & Lopez de Mantaras, R. (1990). A case-based apprentice that learns from fuzzy examples. Methodologies for Intelligent Systems, 5, 420–427.Google Scholar