Approximate Inference for Assumption-based Argumentation in AI[go to overview]
This thesis focuses on approximate inference in assumption-based argumentation frameworks.Argumentation provides a significant idea in the computerization of theoretical and practical reasoning in AI.And it has a close connection with AI, engaging in arguments to perform scientific reasoning.The fundamental approach in this field is abstract argumentation frameworks developed by Dung.Assumption-based argumentation can be regarded as an instance of abstract argumentation withstructured arguments. When facing a large scale of data, a challenge of reasoningin assumption-based argumentation is how to construct arguments and resolve at-tacks over a given claim with minimal cost of computation and acceptable accuracyat the same time. This thesis proposes and investigates approximate methods thatrandomly select and construct samples of frameworks based on graphical disputederivations to solve this problem. The presented approach aims to improve reason-ing performance and get an acceptable trade-off between computational time andaccuracy. The evaluation shows that for reasoning in assumption-based argumenta-tion, in general, the running time is reduced with the cost of slightly low accuracy byrandomly sampling and constructing inference rules for potential arguments over aquery.
15.04.21 - 10:15
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