The objective of this paper is to present developed methodology for Granular Cognitive Map reconstruction. Granular Cognitive Maps model complex imprecise systems. With a proper adjustment of granularity parameters, a Granular Cognitive Map can represent given system with good balance between generality and specificity of the description. The authors present a methodology for Granular Cognitive Map reconstruction. The proposed approach takes advantage of granular information representation model. The objective of optimization is to readjust granularity parameters in order to increase coverage of targets by map responses. In this way we take full advantage of the granular information representation model and produce better, more accurate map, which maintains exactly the same balance between generality and specificity. Proposed methodology reconstructs Granular Cognitive Map without loosing its specificity. Presented approach is applied in a series of experiments that allow evaluating quality of reconstructed maps.
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