Conditional Argumentative Reasoning

条件论证推理

• 批准号: 423456621
• 负责人: Professorin Dr. Gabriele Kern-Isberner
• 金额: --
• 依托单位: Informatik I - Lehrstuhl Logik in der Informatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/423456621?language=en
• 关键词: Conditional; Argumentative; Reasoning

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.

能够根据不确定和矛盾的信息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。这一挑战不仅要求方法能够处理大量的数据，而且还要求方法能够用从数据中挖掘的可废止规则和从这些规则中构造的参数进行象征性推理。在人工智能中，形式论证的研究领域最近越来越受到关注。形式论证的计算模型能够建立、比较和分析论证，从而提供了一种根据矛盾信息进行理性决策支持的方法。相反，其他研究领域解决类似的问题-如默认推理，可撤销推理，特别是条件推理-侧重于规则的作用时，执行推理，特别是规则的适用性的不确定性。为了能够应对处理不确定和矛盾信息的挑战，这两个方面都必须考虑到。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，并集中在以下两个研究问题。首先，在ADF中，通过所谓的接受条件来定义参数的接受。人们可以将这些接受条件解释为规则，这在CL中产生了一个知识库。现在可以应用CL的推理机制-例如System Z -并将结果与原始ADF推理机制进行比较，特别是在一般论证术语中分析结果。其次，CL中的任何知识库都可以以相同的方式解释为ADF。现在可以应用ADF推理机制-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。调查这些将带来关于这两种方法如何联系的见解，更重要的是，它们如何相互受益。这两个研究领域都为具体的推理方法开发了不同的评价标准-例如玩具例子和合理性假设-并且通过我们的翻译，新的标准将分别适用于这两个领域。在本项目中，我们将详细解决上述两个研究问题。更具体地说，我们将开发新的推理机制的ADF的基础上CL推理机制，反之亦然，并评估这些和现有的方法与其他领域提供的评估标准，分别。