Related papers: Classical Planning in Deep Latent Space

Classical Planning in Deep Latent Space

URL: http://arxiv.org/abs/2107.00110v1
Date: Wed, 30 Jun 2021 21:31:21 GMT
Title: Classical Planning in Deep Latent Space
Authors: Masataro Asai, Hiroshi Kajino, Alex Fukunaga, Christian Muise
Abstract summary: Latplan is an unsupervised architecture combining deep learning and classical planning. Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution.
Score: 33.06766829037679
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We evaluate Latplan using image-based versions of 6 planning domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of LightsOut.

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