Conditional Argumentative Reasoning

能够在不确定和矛盾的信息中提供决策支持是现代和未来人工智能系统的核心功能之一。这一挑战不仅需要能够处理大量数据的方法，而且还需要能够使用从数据中挖掘的可行规则和从这些规则中构造的参数进行符号化推理的方法。在人工智能领域，形式论证的研究领域最近得到了越来越多的关注。形式论证的计算模型能够构建、比较和分析论证，从而在矛盾的信息中提供理性决策支持的方法。相比之下，解决类似问题的其他研究领域——如默认推理、可否定推理，特别是条件推理——关注规则在执行推理时的作用，特别是规则适用性的不确定性。为了能够应对处理不确定和矛盾信息的挑战，必须考虑到这两个方面。CAR项目旨在为形式论证和基于规则的推理的综合方法建立理论基础。在技术上，我们将考虑抽象辩证框架（adf）和条件逻辑（CL）的方法，并重点关注以下两个研究问题。首先，在ADF中，参数的接受是通过所谓的接受条件来定义的。可以将这些接受条件解释为规则，从而产生CL中的知识库。现在可以应用CL的推理机制——比如System Z——并将结果与原始的ADF推理机制进行比较，特别是用一般的论证术语分析结果。其次，CL中的任何知识库都可以以同样的方式解释为ADF。现在可以应用ADF推理机制——比如稳定语义——从而为CL定义一个新的推理机制。翻译和研究问题都提供了比较不同方法的方法。对这些问题进行调查将有助于了解这两种方法之间的关系，更重要的是，了解它们如何相互受益。这两个研究领域都为具体的推理方法制定了不同的评估标准，例如玩具示例和合理性假设，通过我们的翻译，这两个领域将分别提供新的标准。在这个项目中，我们将详细解决上述两个研究问题。更具体地说，我们将基于CL推理机制为adf开发新的推理机制，反之亦然，并分别使用其他领域提供的评估标准对这些方法和现有方法进行评估。

Being able to provide decision-support in the light of uncertain and contradictory information is one of the core functionalities of modern and future AI systems. This challenge calls for methods not only capable of handling huge amounts of data but, in addition, methods being able to reason symbolically with both defeasible rules mined from the data and arguments constructed from these rules. Within AI, the research area of formal argumentation has recently gained increasing attention. Computational models of formal argumentation are able to build, compare, and analyse arguments, thus providing an approach for rational decision-support in the light of contradictory information. In contrast, other research areas addressing similar problems---such as default reasoning, defeasible reasoning, and, in particular, conditional reasoning---focus on the role of rules when performing inference and particularly the uncertainty of the applicability of rules. In order to be able to address the challenge of handling both uncertain and contradictory information, both aspects have to be taken into account.The project CAR aims at establishing a theoretical basis for integrative approaches of formal argumentation and rule-based reasoning. Technically, we will consider the approaches of Abstract Dialectical Frameworks (ADFs) and Conditional Logic (CL) and focus on the following two research questions. First, in an ADF, acceptance of arguments is defined through so-called acceptance conditions. One can interpret these acceptance conditions as rules and this yields a knowledge base in CL. Now one can apply reasoning mechanisms from CL - such as System Z - and compare the results with the original ADF reasoning mechanisms and, in particular, analyse the results in general argumentative terms. Second, any knowledge base in CL can be interpreted as an ADF in the same way. Now one can apply ADF reasoning mechanisms---such as stable semantics---and thus define a new reasoning mechanism for CL. Both translations and research questions provide ways to compare the different approaches. Investigating these will bring insights on how these two approaches relate and, more importantly, how they can benefit from each other. Both research areas developed diverse evaluation criteria---such as toy examples and rationality postulates---for concrete reasoning approaches and through our translations, new criteria will be available for both areas, respectively.In this project, we will address both research questions outlined above in detail. More concretely, we will develop novel reasoning mechanisms for ADFs based on CL reasoning mechanisms and vice versa, and evaluate those and existing approaches with evaluation criteria made available by the other area, respectively.