2024/09/25 by Xiao Chi, Chi, Xiao
Business, Management and Accounting · Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Law #Dispute Resolution and Class Actions #FOS: Computer and information sciences #Multi-Agent Systems and Negotiation
paper · pdf · doi:10.48550/arxiv.2409.16854
openalex publication_date 2024/09/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Mediation is often treated as an extension of negotiation, without taking into account the unique role that norms and facts play in legal mediation. Additionally, current approaches for updating argument acceptability in response to changing variables frequently require the introduction of new arguments or the removal of existing ones, which can be inefficient and cumbersome in decision-making processes within legal disputes. In this paper, our contribution is two-fold. First, we introduce a QuAM (Quantitative Argumentation Mediate) framework, which integrates the parties' knowledge and the mediator's knowledge, including facts and legal norms, when determining the acceptability of a mediation goal. Second, we develop a new formalism to model the relationship between the acceptability of a goal argument and the values assigned to a variable associated with the argument. We use a real-world legal mediation as a running example to illustrate our approach.