Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary

TL;DR

This paper introduces LatPlan, an unsupervised deep learning architecture that creates symbolic representations from images to enable classical planning without manual knowledge engineering.

Contribution

It presents a novel combination of deep autoencoders and symbolic planning, including a State Autoencoder and Action Autoencoder, to bridge subsymbolic and symbolic AI.

Findings

Successfully plans in image-based 8-puzzle, Towers of Hanoi, and LightsOut domains.

Automatically learns symbolic representations and action models from raw images.

Demonstrates feasibility of deep learning for symbolic planning without labeled data.

Abstract

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose LatPlan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), and a pair of images representing the initial and the goal states (planning inputs), LatPlan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. The contribution of this paper is twofold: (1) State Autoencoder, which finds a propositional state representation of the environment using a Variational Autoencoder. It generates a discrete latent vector from the images, based on which a PDDL model can be constructed and then solved by an off-the-shelf planner. (2) Action Autoencoder / Discriminator, a neural architecture which jointly finds the action symbols and the implicit action models (preconditions/effects), and provides a successor function for the implicit graph search. We evaluate LatPlan using image-based versions of 3 planning domains: 8-puzzle, Towers of Hanoi and LightsOut.