Using Knowledge about the Opponent in Game-Tree Search.

Ever since Shannon proposed his view on programming a computer to play chess, computer chess researchers have respected his basic assumptions. This thesis relaxes some of these assumptions, in particular the tenet that the two players should be assumed identical or similar. During at least part of any interesting game, one of the players the agent attempts to achieve a result larger than the game-theoretic value e.g. tries to convert a drawn position into a win, or to salvage a loss. This is only possible if the opponent makes a game-theoretic mistake the agent can exploit. Hence the agent will try, based on knowledge of the opponent, to bring about a situation in which the likelihood that the latter commits an error is increased speculative play. The first part of the thesis considers properties of random games, as defined by their game graphs. In the second part of the thesis, some properties defined in part I are discussed for the chess endgame of King and Queen vs. King and Rook KQKR.