An adaptive AI for real-time strategy games

In real-time strategy (RTS) games, the human player faces tasks such as resource allocation, mission planning, and unit coordination. An Artificial Intelligence (AI) system that acts as an opponent against the human player need to be quite powerful, in order to create one cohesive strategy for victory. Even though the goal for an AI system in a computer game is not to defeat the human player, it might still need to act intelligently and look credible. It might however also need to provide just enough difficulty, so that both novice and expert players appreciates the game. The behavior of computer controlled opponents in RTS games of today has to a large extent been based on static algorithms and structures. Furthermore, the AI in RTS games performs the worst at the strategic level, and many of the problems can be tracked to its static nature. By introducing an adaptive AI at the strategic level, many of the problems could possibly be solved, the illusion of intelligence might be strengthened, and the entertainment value could perhaps be increased. The aim of this dissertation has been to investigate how dynamic scripting, a technique for achieving adaptation in computer games, possibly could be applied at the strategic level in an RTS game. The dynamic scripting technique proposed by Spronck, et al. (2003), was originally intended for computer role-playing games (CRPGs), where it was used for online creation of scripts to control non-player characters (NPCs). The focus in this dissertation has been to investigate: (1) how the structure of dynamic scripting possibly could be modified to fit the strategic level in an RTS game, (2) how the adaptation time possibly could be lowered, and (3) how the performance of dynamic scripting possibly could be throttled. A new structure for applying dynamic scripting has been proposed: a goal-rule hierarchy, where goals are used as domain knowledge for selecting rules. A rule is seen as a strategy for achieving a goal, and a goal can in turn be realized by several different rules. The adaptation process operates on the probability of selecting a specific rule as strategy for a specific goal. Rules can be realized by sub-goals, which create a hierarchical system. Further, a rule can be coupled with preconditions, which if false initiates goals with the purpose of fulfilling them. This introduces planning. Results have shown that it can be more effective, with regard to adaptation time, readaptation time, and performance, to have equal punishment and reward factors, or to have higher punishments than rewards, compared to having higher rewards than punishments. It has also been shown that by increasing the learning rate, or including the derivative, both adaptation, and re-adaptation times, can effectively be lowered. Finally, this dissertation has shown that by applying a fitness-mapping function, the performance of the AI can effectively be throttled. Results have shown that learning rate, and maximum weight setting, also can be used to vary the performance, but not to negative performance levels.