Il existe des algorithmes en meilleur d'abord efficace pour la résolution des jeux déterministes à deux joueurs et à deux issues.Nous proposons un cadre formel pour la représentation de tels algorithmes en meilleur d'abord.Le cadre est suffisamment général pour exprimer des algorithmes populaires tels Proof Number Search, Monte Carlo Tree Search, ainsi que l'algorithme Product Propagation.Nous montrons par ailleurs comment adapter ce cadre à deux situations plus générales: les jeux à deux-joueurs à plusieurs issues, et le problème de model checking en logique modale K.Cela donne lieu √† de nouveau algorithmes pour ces situations inspirés des méthodes Proof Number et Monte Carlo.La technique de l'élagage alpha-beta est cruciale dans les jeux à actions séquentielles.Nous proposons une extension de cette techniques aux stacked-matrix games, une généralisation des jeux à deux joueurs, à information parfaite et somme nulle qui permet des actions simultanées. / Efficient best-first search algorithms have been developed for deterministic two-player games with two-outcome.We present a formal framework to represent such best-first search algorithms.The framework is general enough to express popular algorithms such as Proof Number Search, Monte Carlo Tree Search, and the Product Propagation algorithm.We then show how a similar framework can be devised for two more general settings: two-player games with multiple outcomes, and the model checking problem in modal logic K.This gives rise to new Proof Number and Monte Carlo inspired search algorithms for these settings.Similarly, the alpha-beta pruning technique is known to be very important in games with sequential actions.We propose an extension of this technique for stacked-matrix games, a generalization of zero-sum perfect information two-player games that allows simultaneous moves.

Moving target search has been given much attention during the last twenty years. It is a game in which multiple pursuers (cops) try to catch an evading agent (robber) and also known as the game of cops and robber. Within this thesis we study a discrete alternating version played on a graph with given initial positions for the cops and the robber, providing a number of results for optimal and sub-optimal approaches to the game.

The game of Hex is of interest to the mathematics, algorithms, and artificial intelligence communities. It is a classical PSPACE-complete problem, and its invention is intrinsically tied to the Four Colour Theorem and the well-known strategy-stealing argument. Nash, Shannon, Tarjan, and Berge are among the mathematicians who have researched and published about this game. In this thesis we expand on previous research, further developing the mathematical theory and algorithmic techniques relating to Hex. In particular, we identify new classes of moves that can be pruned from consideration, and devise new algorithms to identify connection strategies efficiently. As a result of these theoretical improvements, we produce an automated solver capable of solving all 8 x 8 Hex openings and most 9 x 9 Hex openings; this marks the first time that computers have solved all Hex openings solved by humans. We also produce the two strongest automated Hex players in the world --- Wolve and MoHex --- and obtain both the gold and silver medals in the 2008 and 2009 International Computer Olympiads.

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Efficient best-first search algorithms have been developed for deterministic two-player games with two-outcome.We present a formal framework to represent such best-first search algorithms.The framework is general enough to express popular algorithms such as Proof Number Search, Monte Carlo Tree Search, and the Product Propagation algorithm.We then show how a similar framework can be devised for two more general settings: two-player games with multiple outcomes, and the model checking problem in modal logic K.This gives rise to new Proof Number and Monte Carlo inspired search algorithms for these settings.Similarly, the alpha-beta pruning technique is known to be very important in games with sequential actions.We propose an extension of this technique for stacked-matrix games, a generalization of zero-sum perfect information two-player games that allows simultaneous moves.