Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural NID Rules   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.

11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat