Preference Elicitation in Assumption-Based Argumentation
This work addresses a theoretical problem in structured argumentation for AI researchers, offering a novel algorithmic approach to preference elicitation.
The paper tackles the inverse problem of identifying preferences over assumptions that lead to a given set of conclusions in Assumption-Based Argumentation, presenting an algorithm that computes all such preference sets with proven soundness, completeness, and complexity analysis.
Various structured argumentation frameworks utilize preferences as part of their standard inference procedure to enable reasoning with preferences. In this paper, we consider an inverse of the standard reasoning problem, seeking to identify what preferences over assumptions could lead to a given set of conclusions being drawn. We ground our work in the Assumption-Based Argumentation (ABA) framework, and present an algorithm which computes and enumerates all possible sets of preferences over the assumptions in the system from which a desired conflict free set of conclusions can be obtained under a given semantic. After describing our algorithm, we establish its soundness, completeness and complexity.