Implementation of Intelligent Agent in Defense of the Ancient 2 through Utilization of Opponent Modeling

Intelligent agent is specially suited for completing tasks in video games. Intelligent agent in Dota 2 had been proven to be better at professional human with bot that was developed by OpenAI. Other bots haven’t been able to produce good level of performance as generally bots are developed with rule-based approach. This caused the bot to perform as good as the developer’s understanding of the game. One of learning method to be used in this case is opponent modeling; an attempt at modeling opponent based on its behavior. First, bot will play a round of training match to gather environment data. Model is built based on the data that is gathered with opponent’s current action target as the target regression. Prediction from the model is used in bot for consideration in deciding which action is best against opponent’s action. For validation, implemented bot with opponent modeling faced against bot without opponent modeling and also default bot from Dota 2. The results showed that opponent modeling as a component is able to increase the level of performance on the implemented bot. This showed that opponent modeling is able to provide relevant information for the bot to decide the best action.