Abstract This paper introduces epistemic graphs as a generalization of the epistemic approach to probabilistic argumentation. In these graphs, an argument can be believed or disbelieved up to a given degree, thus providing a more fine–grained alternative to the standard Dung's approaches when it comes to determining the status of a given argument. Furthermore, the flexibility of the epistemic approach allows us to both model the rationale behind the existing semantics as well as completely deviate from them when required. Epistemic graphs can model both attack and support as well as relations that are neither support nor attack. The way other arguments influence a given argument is expressed by the epistemic constraints that can restrict the belief we have in an argument with a varying degree of specificity. The fact that we can specify the rules under which arguments should be evaluated and we can include constraints between unrelated arguments permits the framework to be more context–sensitive. It also allows for better modelling of imperfect agents, which can be important in multi–agent applications.

中文翻译：

---

用于表示和推理的认知图，具有论证的正面和负面影响

摘要 本文介绍了认知图作为概率论证的认知方法的概括。在这些图中，可以在给定的程度上相信或不相信某个论点，从而在确定给定论点的状态时，为标准 Dung 的方法提供了更细粒度的替代方案。此外，认知方法的灵活性使我们既可以对现有语义背后的基本原理进行建模，也可以在需要时完全背离它们。认知图可以对攻击和支持以及既不支持也不攻击的关系进行建模。其他论点影响给定论点的方式由认知约束来表达，这些约束可以限制我们在具有不同程度特异性的论点中的信念。我们可以指定应该评估参数的规则，并且我们可以在不相关的参数之间包含约束，这一事实允许框架对上下文更加敏感。它还允许更好地建模不完美的代理，这在多代理应用程序中可能很重要。