Creating Intelligent Agents in Games

Abstract

Game playing has long been a central topic in artificial intelligence. Whereas early research focused on utilizing search and logic in board games, machine learning in video games is driving much of the recent research. In video games, intelligent behavior can be naturally captured through interaction with the environment, and biologically inspired techniques such as evolutionary computation, neural networks, and reinforcement learning are well suited for this task. In particular, neuroevolution, i.e. constructing artificial neural network agents through simulated evolution, has shown much promise in many game domains. Based on sparse feedback, complex behaviors can be discovered for single agents and for teams of agents, even in real time. Such techniques may allow building entirely new genres of video games that are more engaging and entertaining than current games, and can even serve as training environments for people. Techniques developed in such games may also be widely applicable to other fields, such as robotics, resource optimization, and intelligent assistants. 1