Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148710
Title: Explainable and argumentation-based decision making with qualitative preferences for diagnostics and prognostics of Alzheimer's Disease
Authors: Zeng, Zhiwei
Shen, Zhiqi
Tan, Benny Toh Hsiang
Chin, Jing Jih
Leung, Cyril
Wang, Yu
Chi, Ying
Miao, Chunyan
Keywords: Engineering
Issue Date: 2020
Source: Zeng, Z., Shen, Z., Tan, B. T. H., Chin, J. J., Leung, C., Wang, Y., Chi, Y. & Miao, C. (2020). Explainable and argumentation-based decision making with qualitative preferences for diagnostics and prognostics of Alzheimer's Disease. International Conference on Principles of Knowledge Representation and Reasoning (KR'20), 816-826. https://dx.doi.org/10.24963/kr.2020/84
Abstract: Argumentation has gained traction as a formalism to make more transparent decisions and provide formal explanations recently. In this paper, we present an argumentation-based approach to decision making that can support modelling and automated reasoning about complex qualitative preferences and offer dialogical explanations for the decisions made. We first propose Qualitative Preference Decision Frameworks (QPDFs). In a QPDF, we use contextual priority to represent the relative importance of combinations of goals in different contexts and define associated strategies for deriving decision preferences based on prioritized goal combinations. To automate the decision computation, we map QPDFs to Assumption-based Argumentation (ABA) frameworks so that we can utilize existing ABA argumentative engines for our implementation. We implemented our approach for two tasks, diagnostics and prognostics of Alzheimer's Disease (AD), and evaluated it with real-world datasets. For each task, one of our models achieves the highest accuracy and good precision and recall for all classes compared to common machine learning models. Moreover, we study how to formalize argumentation dialogues that give contrastive, focused and selected explanations for the most preferred decisions selected in given contexts.
URI: https://hdl.handle.net/10356/148710
ISBN: 978-0-9992411-7-2
DOI: 10.24963/kr.2020/84
Rights: © 2020 International Joint Conferences on Artificial Intelligence Organization. All rights reserved. This paper was published in International Conference on Principles of Knowledge Representation and Reasoning (KR'20) and is made available with permission of International Joint Conferences on Artificial Intelligence Organization
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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