Ami Hauptman

GP-EndChess: using genetic programming to evolve chess endgame players

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.

Evolution of an efficient search algorithm for the mate-in-N problem in chess

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 search algorithms successfully solve several instances of the Mate-In-N problem, for the hardest ones developing 47% less gametree nodes than CRAFTY--a state-of-the-art chess engine with a ranking of 2614 points. Improvement is thus not over the basic alpha-beta algorithm, but over a world-class program using all standard enhancements.

Designing an Evolutionary Strategizing Machine for Game Playing and Beyond

We have 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 competitors. This paper has a twofold objective: first, to review our results of applying genetic programming in the domain of games; second, to formulate the merits of genetic programming in acting as a tool for developing strategies in general, and to discuss the possible design of a strategizing machine.

GP-rush: using genetic programming to evolve solvers for the rush hour puzzle

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 (non-evolutionary) search for some instances, but hinder search substantially for many other instances. We then turn to genetic programming (GP) and find that evolution proves immensely efficacious, managing to combine heuristics of such highly variable utility into composites that are nearly always beneficial, and far better than each separate component. GP is thus able to beat both the human player of the game and also the human designers of heuristics.

Evolutionary Design of FreeCell Solvers

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 policy-based, genetic programming. Our evolved solvers outperform the best FreeCell solver to date by three distinct measures: 1) number of search nodes is reduced by over 78%; 2) time to solution is reduced by over 94%; and 3) average solution length is reduced by over 30%. Our top solver is the best published FreeCell player to date, solving 99.65% of the standard Microsoft 32 K problem set. Moreover, it is able to convincingly beat high-ranking human players.

GA-FreeCell: evolving solvers for the game of FreeCell

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 the best published solver to date by three distinct measures: 1) Number of search nodes is reduced by 87%; 2) time to solution is reduced by 93%; and 3) average solution length is reduced by 41%. Our top solver is the best published FreeCell player to date, solving 98% of the standard Microsoft 32K problem set, and also able to beat high-ranking human players.

EMERGENCE OF COMPLEX STRATEGIES IN THE EVOLUTION OF CHESS ENDGAME PLAYERS

We examine a strong chess-endgame player, previously developed by us through genetic programming, focusing on the players emergent capabilities and tactics in the context of a chess match. First, we provide a detailed description of the evolutionary approach by which our player was developed. Then, using a number of methods we analyze the evolved players building blocks and their effect on play level. We conclude that evolution has found combinations of building blocks that are far from trivial and cannot be explained through simple combination — thereby indicating the possible emergence of complex strategies.