Adaptive Goal Oriented Action Planning for RTS Games

This thesis describes the architecture of an adaptive goal-oriented AI system that can be used for Real-Time Strategy games. The system is at the end tested against a single opponent on three di erent maps with di erent sizes to test the ability of the AI opposed to the 'standard' Finite State Machines and the likes in Real-Time Strategy games. The system consists of a task handler agent that manages all the active and halted tasks. A task is either low-level; used for ordering units, or high-level that can form advanced strategies. The General forms plans that are most advantageous at the moment. For creating e ective units against the opponent a priority system is used; where the unit priorities are calculated dynamically