Related papers: Neural NID Rules

Neural NID Rules

URL: http://arxiv.org/abs/2202.06036v1
Date: Sat, 12 Feb 2022 10:47:06 GMT
Title: Neural NID Rules
Authors: Luca Viano and Johanni Brea
Abstract summary: We introduce Neural NID, a method that learns abstract object properties and relations between objects with a suitably regularized graph neural network. We validate the greater generalization capability of Neural NID on simple benchmarks specifically designed to assess the transition dynamics learned by the model.
Score: 2.1030878979833467
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Abstract object properties and their relations are deeply rooted in human common sense, allowing people to predict the dynamics of the world even in situations that are novel but governed by familiar laws of physics. Standard machine learning models in model-based reinforcement learning are inadequate to generalize in this way. Inspired by the classic framework of noisy indeterministic deictic (NID) rules, we introduce here Neural NID, a method that learns abstract object properties and relations between objects with a suitably regularized graph neural network. We validate the greater generalization capability of Neural NID on simple benchmarks specifically designed to assess the transition dynamics learned by the model.

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