论文标题
基于过滤的一般方法，用于学习认知图的理性约束
A Filtering-based General Approach to Learning Rational Constraints of Epistemic Graphs
论文作者
论文摘要
认知图是对概率论证的认知方法的概括。亨特（Hunter）提出了一个2条概括框架，以从众包数据中学习认知约束。但是，学识渊博的认知约束仅反映了用户从数据中的信念，而无需考虑认知图中编码的合理性。同时，当前的框架只能产生认识论的约束，反映了代理人是否相信论点，而不是它所相信的程度。实现这种效果的主要挑战是，在扩大限制因素的种类时，计算复杂性将急剧增加，这可能导致不可接受的时间性能。为了解决这些问题，我们使用多路概括步骤提出了一种基于过滤的方法，以生成一组合理规则，这些规则与数据集中的认知图一致。这种方法能够学习各种各样的理性规则，这些规则反映了域模型和用户模型中的信息。此外，为了提高计算效率，我们引入了一个新功能，以排除毫无意义的规则。经验结果表明，在扩展各种规则时，我们的方法大大优于现有框架。
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.
