Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although recent work in deep learning has achieved impressive results in many fields, the knowledge is encoded in a subsymbolic representation which cannot be directly used by symbolic systems such as planners. We propose LatPlan, an integrated architecture combining deep learning and a classical planner. Given a set of unlabeled training image pairs showing allowed actions in the problem domain, and a pair of images representing the start and goal states, LatPlan uses a Variational Autoencoder to generate 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. We evaluate LatPlan using image-based versions of 3 planning domains: 8-puzzle, LightsOut, and Towers of Hanoi. 0-abstract.tex 1
REF develop a planning architecture capable of planning using only pairs of images (representing, respectively, the initial and goal states) from the domain by converting the images into a latent space representation.
Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary
ArXiv
ArXiv
Computer Science