The Bayesian Approach to Robust Argumentation Machines

众所周知，贝叶斯论证方法（i）具有坚实的规范基础，（ii）与推理和论证心理学实验的经验数据联系良好。这项研究提案的主要目标是证明它也有计算资源，允许在强大的论证机器的背景下进行大规模应用。我们将调整一些可用的计算工具和方法来研究论证，并在需要时开发新的工具和方法。具体而言，我们的项目有以下四个目标：1。使用机器学习工具从大型数据集中学习贝叶斯信念网络（BBN）。2.从这些BBN中开发适当的参数生成和评估算法。3.建立工具来测试生成的论点的感知论点质量。4.使用这些工具来测试我们生成的参数。

It is well-known that the Bayesian approach to argumentation (i) has a solid normative foundation and (ii) connects well with empirical data from experiments in the psychology of reasoning and argumentation. The main objective of this research proposal is to demonstrate that it also has the computational resources to allow for large-scale applications in the context of robust argumentation machines. We will adapt some of the available computational tools and methods to the study of argumentation and develop new tools and methods if needed. More specifically, our project has the following four objectives: 1. To use machine learning tools to learn Bayesian Belief Networks (BBNs) from large data sets. 2. To develop adequate argument generation and evaluation algorithms from these BBNs. 3. To set up tools for testing perceived argument quality of generated arguments. 4. To use these tools to test the arguments we generated.