An explainable multi-attribute decision model based on argumentation. (1st March 2019)

• Record Type: Journal Article
• Authors: Zhong, Qiaoting; Fan, Xiuyi; Luo, Xudong; Toni, Francesca
• Is Part Of: Expert systems with applications. Volume 117(2019)
• Page Start: 42
• Page End: 61
• Publication Date: 2019-03-01
• Subjects: Multi-attribute decision-making -- Explainable artificial intelligence -- Computational argumentation -- Natural language generation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.09.038
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Selecting decisions that achieve most goals but with fewest redundant attributes. An argumentative process to identify best decisions with explanation. Application in literature search in law shows promising results. Natural language explanations are useful for supporting human decision makers. Abstract: We present a multi-attribute decision model and a method for explaining the decisions it recommends based on an argumentative reformulation of the model. Specifically, (i) we define a notion of best ( i.e., minimally redundant) decisions amounting to achieving as many goals as possible and exhibiting as few redundant attributes as possible, and (ii) we generate explanations for why a decision is best or better than or as good as another, using a mapping between the given decision model and an argumentation framework, such that best decisions correspond to admissible sets of arguments. Concretely, natural language explanations are generated automatically from dispute trees sanctioning the admissibility of arguments. Throughout, we illustrate the power of our approach within a legal reasoning setting, where best decisions amount to past cases that are most similar to a given new, open case. Finally, we conduct an empirical evaluation of our method with legal practitioners, confirming that our method is effective for the choice of most similar past cases and helpful to understand automatically generated recommendations .