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Journals and Conferences
We propose an approach for developing efficient search algorithms through genetic programming. Focusing on the game of chess we evolve entire game-tree search algorithms to solve the Mate-In-N problem: find a key move such that even with the best possible counterplays, the opponent cannot avoid being mated in (or before) move N . We show that our evolved… (More)
We apply genetic programming to the evolution of strategies for playing chess endgames. Our evolved programs are able to draw or win against an expert human-based strategy, and draw against CRAFTY—a world-class chess program, which finished second in the 2004 Computer Chess Championship.
We have recently shown that genetically programming game players, after having imbued the evolutionary process with human intelligence, produces human-competitive strategies for three games: backgammon, chess endgames, and robocode (tank-fight simulation). Evolved game players are able to hold their own—and often win—against human or human-based… (More)
In this paper, we evolve heuristics to guide staged deepening search for the hard game of FreeCell, obtaining top-notch solvers for this human-challenging puzzle. We first devise several novel heuristic measures using minimal domain knowledge and then use them as building blocks in two evolutionary setups involving a standard genetic algorithm and… (More)
We investigate a strong chess endgame player, previously evolved by us through genetic programming . Its performance is analyzed across four games, demonstrating the chess-playing capabilities developed through evolution. We end with a discussion of our GP-evolved player’s pros and cons.
We evolve heuristics to guide IDA* search for the 6x6 and 8x8 versions of the Rush Hour puzzle, a PSPACE-Complete problem, for which no efficient solver has yet been reported. No effective heuristic functions are known for this domain, and--before applying any evolutionary thinking--we first devise several novel heuristic measures, which improve… (More)
We use genetic programming to evolve highly successful solvers for two puzzles: Rush Hour and FreeCell. Many NP-Complete puzzles have remained relatively neglected by researchers (see (Kendall, Parkes, and Spoerer 2008) for a review). Among these difficult games we find the Rush Hour puzzle, which was proven to be PSPACEComplete for the general n×n case… (More)
We evolve heuristics to guide staged deepening search for the hard game of FreeCell, obtaining top-notch solvers for this NP-Complete, human-challenging puzzle. We first devise several novel heuristic measures and then employ a Hillis-style coevolutionary genetic algorithm to find efficient combinations of these heuristics. Our results significantly surpass… (More)
We use genetic algorithms to evolve highly successful solvers for two puzzles: FreeCell and Sliding-Tile Puzzle. Discrete puzzles, also known as single-player games, are an excellent problem domain for artificial intelligence research, because they can be parsimoniously described yet are often hard to solve (Pearl 1984). A well-known, highly popular example… (More)