Approximate Inference for Assumption-based Argumentation in AI[go to overview]
Argumentation provides a significant idea in the computerization of theoretical and practical reasoning in AI. It has a close connection with artificial intelligence engaging in arguments to perform scientific reasoning. Assumption-based argumentation (ABA) can be regarded as an instance of abstract argumentation with structured arguments. There is a challenge in ABA when facing a large scale of data on how to construct arguments and resolve attacks over the dispute with minimal cost of computation and acceptable accuracy at the same time. To solve this problem, the approximation inference would be considered as a way that selecting samples from potential arguments over a query to reduce the time with the cost of slightly low accuracy. This presentation is to illustrate the motivation, the goal of the research and methods to do it.
15.10.20 - 10:15
via Big Blue Button