Causality, Argumentation, and Machine Learning

因果关系、推理和机器学习

• 批准号: 375588274
• 负责人: Professor Dr. Kristian Kersting
• 金额: --
• 依托单位: Fachgebiet Maschinelles Lernen
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/375588274?language=en
• 关键词: Causality; Argumentation; Machine; Learning

Classification is the problem of categorizing new observations by using a classifier learnt from already categorized examples. In general, the area of machine learning has brought forth a series of different approaches to deal with this problem, from decision trees to support vector machines and others. Recently, approaches to statistical relational learning even take the perspective of knowledge representation and reasoning into account by developing models on more formal logical and statistical grounds. In this project, we will significantly generalize this reasoning aspect of machine learning towards the use of computational models of argumentation, a popular approach to commonsense reasoning, for reasoning within machine learning. Consider e.g. the following two-step classification approach. In the first step, rule learning algorithms are used to extract frequent patterns and rules from a given data set. The output of this step comprises a huge number of rules (given fairly low confidence and support parameters) and these cannot directly be used for the purpose of classification as they are usually inconsistent with one another. Therefore, in the second step, we interpret these rules as the input for approaches to structured argumentation - more specifically ASPIC+, DeLP, ABA, and deductive argumentation - and probabilistic and other quantitative extensions of those. Using the argumentative inference procedures of these approaches and given a new observation, the classification of the new observation is determined by constructing arguments on top of these rules for the different classes and determining their justification status.More precisely, the project CAML will investigate radically novel machine learning approaches as the one outlined above in detail and develop the new field of "Argumentative Machine Learning" in general: a tight integration of "C"omputational "A"rgumentation und "M"achine "L"earning. This has several benefits. The use of argumentation techniques allows to obtain classifiers, which are by design able to explain their decisions, and therefore addresses the recent need for Explainable AI: classifications are accompanied by a dialectical analysis showing why arguments for the conclusion are preferred to counterarguments; this automatic deliberation, validation, reconstruction and synthesis of arguments helps in assessing trust in the classifier, which is fundamental if one plans to take action based on a prediction. Argumentation techniques in machine learning also allows the easy integration of additional expert knowledge in form of arguments. As there are many different approaches to structured argumentation that take different perspectives on the issue of argumentation, their application in machine learning will provide new insights on their usefulness and allows for a comparison between them on a different level.

分类是使用从已经分类的例子中学习到的分类器对新的观察值进行分类的问题。一般来说，机器学习领域已经提出了一系列不同的方法来处理这个问题，从决策树到支持向量机等等。最近，统计关系学习的方法甚至通过在更正式的逻辑和统计基础上开发模型来考虑知识表示和推理的观点。在这个项目中，我们将把机器学习的推理方面推广到使用论证的计算模型，这是一种常见的常识性推理方法，用于机器学习中的推理。例如，考虑以下两步分类方法。在第一步中，使用规则学习算法从给定数据集中提取频繁的模式和规则。这一步的输出包含大量的规则（给出相当低的置信度和支持参数），这些规则不能直接用于分类目的，因为它们通常彼此不一致。因此，在第二步中，我们将这些规则解释为结构化论证方法的输入-更具体地说是ASPIC+， DeLP， ABA和演绎论证-以及这些方法的概率和其他定量扩展。利用这些方法的论证推理程序，给定一个新的观察结果，通过在这些规则之上为不同的类别构造论证并确定它们的证明状态来确定新观察结果的分类。更准确地说，CAML项目将研究上述详细概述的全新机器学习方法，并总体上开发“论证性机器学习”的新领域：“C”计算“a”论证和“M”机器“L”学习的紧密集成。这有几个好处。论证技术的使用允许获得分类器，这些分类器在设计上能够解释它们的决定，因此解决了最近对可解释人工智能的需求：分类伴随着辩证分析，显示为什么结论的论点比反论点更受欢迎；这种对参数的自动审议、验证、重建和综合有助于评估对分类器的信任，如果计划根据预测采取行动，这是至关重要的。机器学习中的论证技术也允许以论证的形式轻松集成额外的专家知识。由于有许多不同的结构化论证方法，对论证问题采取不同的观点，它们在机器学习中的应用将为它们的有用性提供新的见解，并允许在不同层面上对它们进行比较。