  While classical planning has been an active branch of AI, its applicability
is limited to the tasks precisely modeled by humans. Fully automated high-level
agents should be instead able to find a symbolic representation of an unknown
environment without supervision, otherwise it exhibits the knowledge
acquisition bottleneck. Meanwhile, Latplan (Asai and Fukunaga 2018) partially
resolves the bottleneck with a neural network called State AutoEncoder (SAE).
SAE obtains the propositional representation of the image-based puzzle domains
with unsupervised learning, generates a state space and performs classical
planning. In this paper, we identify the problematic, stochastic behavior of
the SAE-produced propositions as a new sub-problem of symbol grounding problem,
the symbol stability problem. Informally, symbols are stable when their
referents (e.g. propositional values) do not change against small perturbation
of the observation, and unstable symbols are harmful for symbolic reasoning. We
analyze the problem in Latplan both formally and empirically, and propose
"Zero-Suppressed SAE", an enhancement that stabilizes the propositions using
the idea of closed-world assumption as a prior for NN optimization. We show
that it finds the more stable propositions and the more compact
representations, resulting in an improved success rate of Latplan. It is robust
against various hyperparameters and eases the tuning effort, and also provides
a weight pruning capability as a side effect.

Machine Learning;Artificial Intelligence