176 research outputs found
Classical Planning in Deep Latent Space
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.Comment: Under review at Journal of Artificial Intelligence Research (JAIR
Recommended from our members
A comparative study of search and optimization algorithms for the automatic control of physically realistic 2-D animated figures
In the Spacetime Constraints paradigm of animation, the animator specifies what a character should do, and the details of the motion are generated automatically by the computer. Ngo and Marks [11, 12] recently proposed a technique of automatic motion synthesis that uses a massively parallel genetic algorithm to search a space of motion controllers that generate physically realistic motions for 2D articulated figures. In this paper, we describe an empirical study of evolutionary computation algorithms and standard function optimization algorithms that were implemented in lieu of the massively parallel GA in order to find a substantially more efficient search algorithm that would be viable on serial workstations. We discovered that simple search algorithms based on the evolutionary programming paradigm were most efficient in searching the space of motion controllers.Engineering and Applied Science
- …