Evolving a Better Adversary: A Case Study in a German Castle

Mainstream video games are increasingly benefiting from more sophisticated adversarial artificial intelligences. The quality of these synthetic opponents is becoming a significant competitive advantage that was once exclusively reserved for graphics. This new generation of synthetic opponent relies on dynamic planning systems such as STRIPS to realize realistic and challenging adversarial behavior. Such systems have been embraced by game developers as they provide for transparent representation of agent state and behavior, have low CPU utilization and are available in toolkit form. Concurrently, the dramatic proliferation of parallel computational units in modern hardware architectures is also facilitating the use of connectionist models of artificial intelligence in gaming. However, significant barriers such as neural network training set generation and turnaround time for neural network development have inhibited widespread adoption of such techniques. To overcome these barriers, we present an infrastructure that automates neural network development through the use of a genetic algorithm to evolve the behavioral training set of an adversarial artificial intelligence. The infrastructure uses an existing game, Wolfenstein 3D, as a simulation environment. We compare the effectiveness of the neural network generated by this system against a manually constructed neural network and the original game AI. All three models are pitted against human players