Influence diagrams (IDs) are well-known formalisms extending Bayesian networks to model decision situations under uncertainty. Although they are convenient as a decision theoretic tool, their knowledge representation ability is limited in capturing other crucial notions such as logical consistency. We complement IDs with the light-weight description logic (DL) EL to overcome such limitations. We consider a setup where DL axioms hold in some contexts, yet the actual context is uncertain. The framework benefits from the convenience of using DL as a domain knowledge representation language and the modelling strength of IDs to deal with decisions over contexts in the presence of contextual uncertainty. We define related reasoning problems and study their computational complexity.

中文翻译：

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使用上下文知识和影响图进行推理

影响图 (ID) 是众所周知的形式主义，它扩展了贝叶斯网络以模拟不确定性下的决策情况。尽管它们作为决策理论工具很方便，但它们的知识表示能力在捕捉其他关键概念（例如逻辑一致性）方面受到限制。我们用轻量级描述逻辑 (DL) EL 补充 ID 以克服这些限制。我们考虑了一种设置，其中 DL 公理在某些上下文中成立，但实际上下文是不确定的。该框架受益于使用 DL 作为领域知识表示语言的便利性和 ID 的建模强度，以在存在上下文不确定性的情况下处理上下文决策。我们定义了相关的推理问题并研究了它们的计算复杂性。