Institute for Web Science and Technologies · Universität Koblenz - Landau
Institute WeST

Using neural networks for approximate approaches as a heuristic to exact methods with abstract argumentation frameworks.

[zur Übersicht]
Heygon Araújo

argumentation is a method for providing abstractions of problems along three spectrums: arguments, attacks, and acceptability, the latter the most important property of a semantic. Exact approaches, often using reduction to some other formalism such as SAT and ASP, are known to be computationally hard and hence, difficult to solve for realistic models. In addressing those issues, this research firstly implements neural networks to predict credulous acceptability of abstract arguments, as a classification problem. Secondly, we propose to implement an efficient heuristic by using the approximate method to set warm-start point, minimize backtracks steps and maximize the performance of a complete solution, so-called DREED, to abstract argumentation problems. To the best of our knowledge, this combination has not been explored yet within the argumentation community.


22.07.21 - 10:15
via Big Blue Button