Authors: Kittisak Potisartra, Vishnu Kotrajaras
Research in turn-based strategy (TBS) games mostly involves classic games, such as Chess, and how such games could be beaten by a computer controlled artificial intelligence. Guaranteeing thatopponents will be beaten, however, is not the focus of commercial Turn-based Strategy games. For commercial games, if human players do not win, they quit the game. This can result in horrific future sales. Therefore, keeping players engage in the game is much more important. This paper presents an artificial player that learns to adjust its skills to match a player it is playing against. A Final Fantasy Tactics-like game is used in our experiment. We introduce evaluation functions for calculating the score from each unit's action. By evaluating a human player's score, our artificial player can estimate his skill and play at the same level throughout the game.