Hybrid Intelligent Decision Support Using a Semiotic Case-Based Reasoning and Self-Organizing Maps

Abstract

Human decision-making involves cognitive processes of selection, evaluation, and interpretation among candidate solutions in order to solve decision problems. Nonintelligent decision support systems (DSS) lack automatic interpretations, at least in a low level scale, which can lead to undesired solutions. To tackle this limitation, hence producing enhanced decision making, a hybrid intelligent decision support approach is presented, which combines case-based reasoning cycle, semiotic concepts, and self-organizing maps. In addition, a novel sign deconstruction mechanism is introduced as foundation of the new approach and affords better interpretability and contextualization of candidate solutions without compromising efficiency and precision. The obtained results confirm that our proposed approach has the potential to be readily applicable to decision problems, particularly the ones that are of subjective nature. Moreover, the put forward approach may integrate some unlikely elements of linguistics and cognitive science which could fundamentally help the enhancement of DSS.