Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103315
Title: Computing argumentative explanations in bipolar argumentation frameworks
Authors: Miao, Chunyan
Leung, Cyril
Shen, Zhiqi
Chin, Jing Jih
Zeng, Zhiwei
Keywords: Argumentation
Explainable Artificial Intelligence
Social sciences::Sociology
Issue Date: 2019
Source: Zeng, Z., Miao, C., Leung, C., Shen, Z., & Chin, J. J. (2019). Computing argumentative explanations in bipolar argumentation frameworks. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 3310079-10080. doi:10.1609/aaai.v33i01.330110079
Abstract: The process of arguing is also the process of justifying and explaining. Transparent reasoning process endows argumentation good explainability. Recently, more research efforts have been devoted to realizing the explanatory power of argumentation in unipolar argumentation frameworks. In addition to the attack relation, bipolar frameworks consider the support relation, which brings greater expressibility but also complexity. It is worth exploring how the interactions encompassed in the support relation contribute to the arguing process and how to capture them in explanations. In this paper, we propose a “stronger” notion of defence and a new bipolar admissibility semantics, which are defined based on both the attack and the support relations, and use them to formalize two types of explanations, namely concise and strong explanations. We then present complete and sound processes for computing explanations by constructing bipolar dispute trees.
URI: https://hdl.handle.net/10356/103315
http://hdl.handle.net/10220/49774
DOI: http://dx.doi.org/10.1609/aaai.v33i01.330110079
Rights: © 2019 Association for the Advancement of Artificial Intelligence (AAAI). All rights reserved. This paper was published in The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) and is made available with permission of Association for the Advancement of Artificial Intelligence (AAAI).
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