  Computing goal-directed behavior is essential to designing efficient AI
systems. Due to the computational complexity of planning, current approaches
rely primarily upon hand-coded symbolic action models and hand-coded
heuristic-function generators for efficiency. Learned heuristics for such
problems have been of limited utility as they are difficult to apply to
problems with objects and object quantities that are significantly different
from those in the training data. This paper develops a new approach for
learning generalized heuristics in the absence of symbolic action models using
deep neural networks that utilize an input predicate vocabulary but are
agnostic to object names and quantities. It uses an abstract state
representation to facilitate data efficient, generalizable learning. Empirical
evaluation on a range of benchmark domains show that in contrast to prior
approaches, generalized heuristics computed by this method can be transferred
easily to problems with different objects and with object quantities much
larger than those in the training data.
