The Adaptive Learning Mechanism Design for Game Agents' Real-time Behavior Control

摘要

In this paper, we present an approach of adaptive learning mechanism for game agents' real-time behavior control. This approach mainly focuses on how to generate game agent's adaptability in real-time. It is possible to apply our approach in complicated game character interactions by following the framework discussed in this paper. We consider the layered architecture, the behavior pattern and the adaptive mechanism design to be the three key points of our approach. We provide a brief example of how to apply adaptive learning in game agents' behavior processing. From this example, we demonstrate that the planning and learning process is fast enough to have 3D model rendered in time.