论文标题
通过上下文知识和影响图的推理
Reasoning with Contextual Knowledge and Influence Diagrams
论文作者
论文摘要
影响图（ID）是众所周知的形式主义，扩展了贝叶斯网络以模拟不确定性下的决策情况。尽管它们作为一种决策理论工具很方便，但他们的知识表示能力在捕获其他关键概念（例如逻辑一致性）方面受到限制。我们将ID与轻重量描述逻辑（DL）EL相辅相成，以克服此类局限性。我们考虑了DL公理在某些情况下保持的设置，但实际上下文尚不确定。该框架受益于将DL用作域知识表示语言的便利性以及ID的建模强度在存在上下文不确定性的情况下在上下文中处理决策。我们定义相关的推理问题并研究其计算复杂性。
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.
