CABARET: rule interpretation in a hybrid architecture
International Journal of Man-Machine Studies - AI and legal reasoning. Part 1
Signal flow graphs vs fuzzy cognitive maps in application to qualitative circuit analysis
International Journal of Man-Machine Studies
A case-based apprentice that learns from fuzzy examples
Methodologies for intelligent systems, 5
Control-Sensitive Feature Selection for Lazy Learners
Artificial Intelligence Review - Special issue on lazy learning
Artificial Intelligence Review - Special issue on lazy learning
Supporting business process redesign using cognitive maps
Decision Support Systems
Case-Based Reasoning Support for Online Catalog Sales
IEEE Internet Computing
A Reflective Architecture for Integrated Memory-Based Learning and Reasoning
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Protein Structure from Contact Maps: A Case-Based Reasoning Approach
Information Systems Frontiers
The Use of Cognitive Maps and Case-Based Reasoning for B2B Negotiation
Journal of Management Information Systems
Global optimization of case-based reasoning for breast cytology diagnosis
Expert Systems with Applications: An International Journal
Contextual fuzzy cognitive map for decision support in geographic information systems
IEEE Transactions on Fuzzy Systems
On causal inference in fuzzy cognitive maps
IEEE Transactions on Fuzzy Systems
Dynamical cognitive network - an extension of fuzzy cognitive map
IEEE Transactions on Fuzzy Systems
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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.