In this work, we propose a Monte Carlo Tree Search (MCTS) based approach to procedurally generate Sokoban puzzles. Our method generates puzzles through simulated game play, guaranteeing solvability in all generated puzzles. We perform a user study to infer features that are efficient to compute and are highly correlated with expected puzzle difficulty. We combine several of these features into ...

We prove the computational intractability of rotating and placing n square tiles into a 1×n array such that adjacent tiles are compatible—either equal edge colors, as in edge-matching puzzles, or matching tab/pocket shapes, as in jigsaw puzzles. Beyond basic NP-hardness, we prove that it is NP-hard even to approximately maximize the number of placed tiles (allowing blanks), while satisfying the...

To demonstrate our approaches we will use Sudoku puzzles, which are an excellent test bed for evolutionary algorithms. The puzzles are accessible enough for people to enjoy. However the more complex puzzles require thousands of iterations before an evolutionary algorithm finds a solution. If we were attempting to compare evolutionary algorithms we could count their iterations to solution as an ...

This paper proposes a model of learning by discovery. The model consists of a program which discovers macro operators while conducting a best first heuristic search in the domain of puzzles. This work extends some recent work on permutation puzzles (Korf, 1982) and operator-decomposable puzzles (Korf, 1983), and is related to the earlier work on MACROPS (Fikes, Hart, and Nilsson, 1972). This wo...