Argumentation Semantics

Argumentation semantics focuses on formally representing and evaluating arguments, aiming to understand how arguments interact and determine which are accepted or rejected. Current research explores various semantics, including extension-based (accepting sets of arguments) and ranking-based (assigning acceptability scores), often applied to both abstract and structured argumentation frameworks. This field is increasingly leveraging machine learning techniques, such as inductive logic programming and neural networks, to improve the efficiency and interpretability of argumentation solvers and to analyze natural language arguments. The resulting advancements have implications for improving AI reasoning capabilities, facilitating human-computer dialogue, and enabling more robust analysis of complex debates.