  Recently, there is an increasing interest in obtaining the relational
structures of the environment in the Reinforcement Learning community. However,
the resulting "relations" are not the discrete, logical predicates compatible
to the symbolic reasoning such as classical planning or goal recognition.
Meanwhile, Latplan (Asai and Fukunaga 2018) bridged the gap between
deep-learning perceptual systems and symbolic classical planners. One key
component of the system is a Neural Network called State AutoEncoder (SAE),
which encodes an image-based input into a propositional representation
compatible to classical planning. To get the best of both worlds, we propose
First-Order State AutoEncoder, an unsupervised architecture for grounding the
first-order logic predicates and facts. Each predicate models a relationship
between objects by taking the interpretable arguments and returning a
propositional value. In the experiment using 8-Puzzle and a photo-realistic
Blocksworld environment, we show that (1) the resulting predicates capture the
interpretable relations (e.g. spatial), (2) they help obtaining the compact,
abstract model of the environment, and finally, (3) the resulting model is
compatible to symbolic classical planning.
