Assumption Based Argumentation

Assumption-based argumentation (ABA) is a formal framework for representing and reasoning with arguments, particularly those involving defeasible or uncertain knowledge. Current research focuses on automating the learning of ABA frameworks from data, using techniques like answer set programming and integrating ABA with other formalisms such as case-based reasoning and probabilistic logic programming to improve model interpretability and performance in applications like causal discovery and decision-making. This work is significant because it enhances the ability to model and reason with complex, uncertain information, leading to more robust and explainable AI systems across various scientific and practical domains.