Causality, Argumentation, and Machine Learning

分类是通过使用从已经分类的样本中学习的分类器来对新的观察结果进行分类的问题。总的来说，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形式化的逻辑和统计基础上开发模型来考虑知识表示和推理的角度。在这个项目中，我们将显著地将机器学习的推理方面推广到使用论证的计算模型，这是一种流行的常识推理方法，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的数据集中提取频繁模式和规则。这一步骤的输出包括大量规则(给定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数量扩展。利用这些方法的论证推理过程并给出一个新的观察，新观察的分类是通过在这些规则上为不同的类构造论点并确定它们的证明状态来确定的。更准确地说，CAML项目将详细地研究上述全新的机器学习方法，并从总体上开发“议论性机器学习”的新领域：“C”假设“A”“推荐”和“M”机器“L”的紧密结合。这有几个好处。论证技术的使用允许获得量词，这些量词经过设计能够解释他们的决定，因此满足了最近对可解释人工智能的需求：分类伴随着辩证分析，表明为什么支持结论的论点比反驳论点更受欢迎；这种论点的自动审议、验证、重构和合成有助于评估对量词的信任，如果一个人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还允许以论证的形式轻松地整合额外的专家知识。由于结构化论证有许多不同的方法，它们在论证问题上采取不同的观点，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进行比较。

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.