Abstract
Procedural generation of initial states of state-space
search problems have applications in human and
machine learning as well as in the evaluation of
planning systems. In this paper we deal with the
task of generating hard and solvable initial states
of Sokoban puzzles. We propose hardness metrics
based on pattern database heuristics and the use of
novelty to improve the exploration of search methods in the task of generating initial states. We then
present a system called ? that uses our hardness
metrics and novelty to generate initial states. Experiments show that ? is able to generate initial states
that are harder to solve by a specialized solver than
those designed by human experts