Substantial efforts have been made in developing various Decision Modeling formalisms, both from industry and academia. A challenging problem is that of expressing decision knowledge in the context of incomplete knowledge. In such contexts, decisions depend on what is known or not known. We argue that none of the existing formalisms for modeling decisions are capable of correctly capturing the epistemic nature of such decisions, inevitably causing issues in situations of uncertainty. This paper presents a new language for modeling decisions with incomplete knowledge. It combines three principles: stratification, autoepistemic logic, and definitions. A knowledge base in this language is a hierarchy of epistemic theories, where each component theory may epistemically reason on the knowledge in lower theories, and decisions are made using definitions with epistemic conditions.

翻译：从工业界和学术界均投入了大量精力开发各种决策建模形式化方法。一个具有挑战性的问题在于如何在知识不完全的背景下表达决策知识。在此类背景下，决策取决于已知或未知的内容。我们认为，现有的决策建模形式化方法均无法准确捕捉此类决策的认知本质，因此在不确定性情境中不可避免地会引发问题。本文提出了一种用于在不完全知识背景下建模决策的新语言。它融合了三种原则：分层、自认知逻辑和定义。该语言中的知识库是一个认知理论的层次结构，其中每个组成理论可以对较低层次理论中的知识进行认知推理，而决策则通过带有认知条件的定义来制定。