Epistemic graphs are a generalization of the epistemic approach to probabilistic argumentation. Hunter proposed a 2-way generalization framework to learn epistemic constraints from crowd-sourcing data. However, the learnt epistemic constraints only reflect users' beliefs from data, without considering the rationality encoded in epistemic graphs. Meanwhile, the current framework can only generate epistemic constraints that reflect whether an agent believes an argument, but not the degree to which it believes in it. The major challenge to achieving this effect is that the computational complexity will increase sharply when expanding the variety of constraints, which may lead to unacceptable time performance. To address these problems, we propose a filtering-based approach using a multiple-way generalization step to generate a set of rational rules which are consistent with their epistemic graphs from a dataset. This approach is able to learn a wider variety of rational rules that reflect information in both the domain model and the user model. Moreover, to improve computational efficiency, we introduce a new function to exclude meaningless rules. The empirical results show that our approach significantly outperforms the existing framework when expanding the variety of rules.

翻译：认知图是概率论证认知方法的一种泛化。Hunter提出了一种双向泛化框架，用于从众包数据中学习认知约束。然而，所学习的认知约束仅反映数据中用户的信念，而未考虑认知图中蕴含的理性。同时，当前框架只能生成反映智能体是否相信某个论证的认知约束，而无法体现其相信程度。实现这一效果的主要挑战在于，当扩展约束种类时计算复杂度会急剧增加，可能导致不可接受的时间性能。为解决这些问题，我们提出一种基于过滤的方法，通过多向泛化步骤从数据集中生成一组与其认知图一致的理性规则。该方法能够学习更丰富的理性规则，这些规则同时反映领域模型和用户模型中的信息。此外，为提高计算效率，我们引入新函数来排除无意义的规则。实验结果表明，在扩展规则种类时，我们的方法显著优于现有框架。