3,250 research outputs found
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
Neural-symbolic computing has now become the subject of interest of both
academic and industry research laboratories. Graph Neural Networks (GNN) have
been widely used in relational and symbolic domains, with widespread
application of GNNs in combinatorial optimization, constraint satisfaction,
relational reasoning and other scientific domains. The need for improved
explainability, interpretability and trust of AI systems in general demands
principled methodologies, as suggested by neural-symbolic computing. In this
paper, we review the state-of-the-art on the use of GNNs as a model of
neural-symbolic computing. This includes the application of GNNs in several
domains as well as its relationship to current developments in neural-symbolic
computing.Comment: Updated version, draft of accepted IJCAI2020 Survey Pape
Goal-Aware Neural SAT Solver
Modern neural networks obtain information about the problem and calculate the
output solely from the input values. We argue that it is not always optimal,
and the network's performance can be significantly improved by augmenting it
with a query mechanism that allows the network at run time to make several
solution trials and get feedback on the loss value on each trial. To
demonstrate the capabilities of the query mechanism, we formulate an
unsupervised (not depending on labels) loss function for Boolean Satisfiability
Problem (SAT) and theoretically show that it allows the network to extract rich
information about the problem. We then propose a neural SAT solver with a query
mechanism called QuerySAT and show that it outperforms the neural baseline on a
wide range of SAT tasks
- …