Improving game-tree search by incorporating error propagation and social orientations

Game-tree search algorithms, such as the two-player Minimax algorithm and its multi-player counterpart, Max-n, are a fundamental component in the development of computer programs for playing extensive-form games. The success of these algorithms is limited by the underlying assumptions on which they are built. For example, it is traditionally assumed that deeper search always produces better decisions and also that search procedures can assume all players are selfish and ignore social orientations. Deviations from these assumptions can occur in real games and can affect the success of a traditional search algorithms. The goal of my thesis is to determine when such deviations occur and modify the search procedure to correct the errors that are introduced.