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R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning
[article]
2022
arXiv
pre-print
Systematicity, i.e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical to machine intelligence. A model with strong systematicity is able to train on small-scale tasks and generalize to large-scale tasks. In this paper, we propose R5, a relational reasoning framework based on reinforcement learning that reasons over relational graph data and explicitly mines underlying compositional logical rules from observations. R5 has
arXiv:2205.06454v1
fatcat:563zfaxiarcflhgwspxy7x7cdm