140695 - Neural-symbolic probabilistic argumentation machines.pdf (564.1 kB)
Neural-symbolic probabilistic argumentation machines
conference contribution
posted on 2023-05-23, 14:43 authored by Riveret, R, Son TranSon Tran, d'Avila Garcez, ANeural-symbolic systems combine the strengths of neural networks and symbolic formalisms. In this paper, we introduce a neural-symbolic system which combines restricted Boltzmann machines and probabilistic semi-abstract argumentation. We propose to train networks on argument labellings explaining the data, so that any sampled data outcome is associated with an argument labelling. Argument labellings are integrated as constraints within restricted Boltzmann machines, so that the neural networks are used to learn probabilistic dependencies amongst argument labels. Given a dataset and an argumentation graph as prior knowledge, for every example/case K in the dataset, we use a so-called K- maxconsistent labelling of the graph, and an explanation of case K refers to a K-maxconsistent labelling of the given argumentation graph. The abilities of the proposed system to predict correct labellings were evaluated and compared with standard machine learning techniques. Experiments revealed that such argumentation Boltzmann machines can outperform other classification models, especially in noisy settings.
History
Publication title
Proceedings of the 17th International Conference Principles of Knowledge Representation and ReasoningPagination
871-881Department/School
School of Information and Communication TechnologyPublisher
IJCAI OrganizationPlace of publication
California, United StatesEvent title
17th International Conference Principles of Knowledge Representation and ReasoningEvent Venue
Rhodes, GreeceDate of Event (Start Date)
2020-09-12Date of Event (End Date)
2020-09-18Rights statement
Copyright 2020 2020 International Joint Conferences on Artificial Intelligence OrganizationRepository Status
- Open