A Novel Computational Workflow for Argumentation in AI

In this project we investigate ways of performing automated reasoning based on argument structures. Within the area of Artificial Intelligence, our field of study is situated in Computational Argumentation. In this field one studies how to represent arguments, what are relationships between arguments, and how to automatically calculate reasoning, based on such arguments. Arguments, as we see or hear them in everyday life, have vary different forms, structures, and aims: for instance, they can be based on authority (arguing by ones expertise), they can connect emotionally (appeal to emotions), or argue on rational grounds (using, e.g., logical reasoning), to name some examples. Structurally, they can be simple or very complex. For our aim of performing automated reasoning on argument structures, we rely on formal specifications of arguments and their inter-relationship (e.g., arguments countering or supporting one another). Such formal specifications allow for implementation of a program that computes argumentative outcomes. In Computational Argumentation, many ways of defining structures of arguments have been proposed, to capture various aspects of argumentation. Importantly, the most common approaches here assume that one constructs arguments and their relationships from given knowledge, and one computes argumentative outcomes after having constructed these arguments. That is, one tries to formulate all ways in which one can argue, based on ones knowledge. While this is a conceptually clean way, in general it can be the case that many arguments are constructed in this way. Having many arguments can be a barrier to computation (the computer has to consider many arguments), and a large number of arguments can be a barrier to human understanding. In our project, we study ways of performing argumentative reasoning differently: in contrast to many earlier approaches, we aim to calculate argumentative reasoning, at first, without making arguments explicit. While this is challenging without access to the concrete arguments, preliminary research shows that with carefully looking at ones knowledge one can reason without explicated arguments. We then, after having concluded what should be the argumentative outcome, plan to construct only arguments required to show the result. In this way, we aim for a novel workflow: compute the result on the original knowledge base and present arguments only when required. For this to be realized, we need to understand argumentative reasoning at a deep level, which is a major challenge for our research project. Based on this, we aim to provide academic prototypes of our approach, both for showing strengths and potential weaknesses of our approaches, and for others to experiment on. We think that our research project can support Computational Argumentation by providing novel insights into computation of argumentative outcomes, and new academic tools.