In this paper, we present the information graph (IG) formalism, which provides a precise account of the interplay between deductive and abductive inference and causal and evidential information, where ‘deduction’ is used for defeasible ‘forward’ inference. IGs formalise analyses performed by domain experts in the informal reasoning tools they are familiar with, such as mind maps used in crime analysis. Based on principles for reasoning with causal and evidential information given the evidence, we impose constraints on the inferences that may be performed with IGs. Our IG-formalism is intended to facilitate the construction of formal representations within AI systems by serving as an intermediary formalism between analyses performed using informal reasoning tools and formalisms that allow for formal evaluation. In this paper, we investigate the use of the IG-formalism as an intermediary formalism in facilitating Bayesian network (BN) graph construction. We propose a structured approach for automatically constructing from an IG a directed BN graph, together with qualitative constraints on the probability distribution represented by the BN. Moreover, we prove a number of formal properties of our approach and identify assumptions under which the construction of an initial BN graph can be fully automated.

在本文中，我们提出了信息图 (IG) 形式主义，它提供了演绎推理和溯因推理与因果和证据信息之间相互作用的精确说明，其中“演绎”用于可废止的“前向”推理。IG 将领域专家在他们熟悉的非正式推理工具中执行的分析正式化，例如犯罪分析中使用的思维导图。基于对给定证据的因果和证据信息进行推理的原则，我们对可能使用 IG 执行的推理施加了限制。我们的 IG 形式主义旨在通过充当使用非正式推理工具执行的分析和允许正式评估的形式主义之间的中介形式主义，来促进人工智能系统内正式表示的构建。在本文中，我们研究了使用 IG 形式主义作为促进贝叶斯网络 (BN) 图构建的中介形式主义。我们提出了一种结构化方法，用于从 IG 自动构建有向 BN 图，以及对 BN 表示的概率分布的定性约束。此外，我们证明了我们方法的许多形式属性，并确定了可以完全自动化构建初始 BN 图的假设。