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.

中文翻译：

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使用基于符号案例的推理和自组织地图的混合智能决策支持

人类决策涉及为了解决决策问题而对候选解决方案进行选择、评估和解释的认知过程。非智能决策支持系统 (DSS) 缺乏自动解释，至少在低级别范围内，这可能导致不受欢迎的解决方案。为了解决这个限制，从而产生增强的决策，提出了一种混合智能决策支持方法，它结合了基于案例的推理循环、符号概念和自组织图。此外，引入了一种新的符号解构机制作为新方法的基础，并在不影响效率和精度的情况下提供了更好的候选解决方案的可解释性和上下文化。获得的结果证实，我们提出的方法具有适用于决策问题的潜力，尤其是那些具有主观性的决策问题。此外，所提出的方法可能会整合语言学和认知科学中一些不太可能的元素，这可以从根本上帮助增强 DSS。