Evolving Artificial Neural Networks To Imitate Human Behaviour In Shinobi III : Return of the Ninja Master
This work addresses the challenge of exploring alternative AI methods beyond deep learning for complex tasks like video game behavior imitation, though it appears incremental in its approach.
The paper tackled the problem of applying Evolutionary Algorithms and Dynamic Neural Networks to complex tasks, achieving agents that perform well on benchmarks and closely imitate human behavior in the video game Shinobi III, a task previously difficult for non-gradient-based methods.
Our society is increasingly fond of computational tools. This phenomenon has greatly increased over the past decade following, among other factors, the emergence of a new Artificial Intelligence paradigm. Specifically, the coupling of two algorithmic techniques, Deep Neural Networks and Stochastic Gradient Descent, thrusted by an exponentially increasing computing capacity, has and is continuing to become a major asset in many modern technologies. However, as progress takes its course, some still wonder whether other methods could similarly or even more greatly benefit from these various hardware advances. In order to further this study, we delve in this thesis into Evolutionary Algorithms and their application to Dynamic Neural Networks, two techniques which despite enjoying many advantageous properties have yet to find their niche in contemporary Artificial Intelligence. We find that by elaborating new methods while exploiting strong computational resources, it becomes possible to develop strongly performing agents on a variety of benchmarks but also some other agents behaving very similarly to human subjects on the video game Shinobi III : Return of The Ninja Master, typical complex tasks previously out of reach for non-gradient-based optimization.