Evolving Solvers for FreeCell and the Sliding-Tile Puzzle

Elyasaf, Achiya (Ben-Gurion University of the Negev) | Zaritsky, Yael (Ben-Gurion University of the Negev) | Hauptman, Ami (Ben-Gurion University of the Negev) | Sipper, Moshe (Ben-Gurion University of the Negev)

AAAI Conferences 

Herein, we employ a genetic algorithm (GA) to obtaining solvers for both the difficult FreeCell puzzle and the slidingtile Discrete puzzles, also known as single-player games, are puzzle. Note that although from a computationalcomplexity an excellent problem domain for artificial intelligence research, point of view the Rush Hour puzzle is harder because they can be parsimoniously described yet (unless NP PSPACE), search spaces induced by typical instances are often hard to solve (Pearl 1984). A well-known, highly of FreeCell tend to be substantially larger than those popular example within the domain of discrete puzzles is the of Rush Hour, and thus far more difficult to solve. This is card game of FreeCell. Another highly popular game is the evidenced by the failure of standard search methods to solve sliding-tile puzzle, the traditional versions of which are the FreeCell, as opposed to their success in solving all 6x6 Rush 15-puzzle (4X4) and the 24-puzzle (5X5). State-of-the-art Hour problems without requiring any heuristics (Hauptman heuristics allow for fast solutions of arbitrary instances of et al. 2009).

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