Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88889
Title: Context-based and explainable decision making with argumentation
Authors: Zeng, Zhiwei
Fan, Xiuyi
Miao, Chunyan
Leung, Cyril
Chin, Jing Jih
Ong, Yew Soon
Keywords: Decision Making
Context-awareness
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2018
Source: Zeng, Z., Fan, X., Miao, C., Leung, C., Chin, J. J., & Ong, Y. S. (2018). Context-based and explainable decision making with argumentation. Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018).
Abstract: Argumentation-based approaches to decision making have gained considerable research interest, due to their ability to select and justify decisions. In order to make better decisions, context is a key piece of information that needs to be considered. However, most existing argumentation-based models and frameworks have not modelled or reasoned with context explicitly. In this paper, we present a new argumentation-based approach for making context-based and explainable decisions. We propose a graphical representation for modelling decision problems involving varying contexts, Decision Graphs with Context (DGC), and a reasoning mechanism for making context-based decisions which relies on the Assumption-based Argumentation formalism. Based on these constructs, we introduce two types of explanations, argument explanation and context explanation, identifying the reasons for the decisions made from an argument-view and a context-view respectively.
URI: https://hdl.handle.net/10356/88889
http://hdl.handle.net/10220/49589
Rights: © 2018 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved. This paper was published in Proceedings of 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018) and is made available with permission of International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).
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