Associative Learning for Enhancing Autonomous Bots in Videogame Design
The Today’s video games are highly technologically advanced, giving users the ability to step into virtual realities and play games from the viewpoint of highly complex characters. Most of the current efforts in the development of believable bots in videogames — bots that behave like human players — are based on classical AI techniques. Specifically, we design virtual bots using Continuous-Time Recurrent Neural Network (CTRNNs) as the controllers of the non-player characters, and we add a learning module to make an agent be capable of re-learning during its lifetime. Agents controlled by CTRNNs are evolved to search for the base camp and the enemy’s camp and associate them with one of two different altitudes depending on experience.We analyze the best-evolved agent’s behavior and explain how it arises from the dynamics of the coupled agent-environment system. The ultimate goal of the contest would be to develop a computer game bot able to behave the same way humans do.
KeywordsVideo Game Associative Learn Recurrent Neural Network Action Video Game Base Camp
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