SBIR Phase II: Evolving Object Neural Networks

这个小型企业创新研究第二阶段的研究项目将研究生成进化对象神经网络的问题，以控制娱乐软件类中的角色，并考虑大型多人在线游戏的类型。研究的目的是确定和开发通用的自适应例程和软件工具，这些例程和软件工具可以整合到软件开发人员工具包(SDK)中，适合于授权给第三方开发人员。在统计框架内进行的一系列实验将确定游戏角色进化控制的参数选择的一阶和二阶效应，这将被纳入SDK。研发的目标是为游戏角色生成最快速的进化学习，同时拥有最小的代码“足迹”。更多的研究将促进自动游戏测试和游戏中人工智能的优化。通过仔细研究研究环境中参数选择的非线性影响，将增强对混合进化计算和神经网络的科学和技术理解。如果成功，该产品将简化视频游戏从开发到产品的过渡。SDK的开发将有助于将视频游戏制作环节的时间和成本降低50%-80%。所开发的软件可以作为大学课程的教学课堂辅助工具。此外，视频游戏和军事模拟之间的强烈相关性表明，它对作战模拟中的动态规划以及对优化商业运营中的行动方案(如供应链管理)做出了重要贡献。

This Small Business Innovation Research Phase II research project will investigate the problem of generating evolutionary object neural networks for controlling characters in classes of entertainment software, with consideration given to genres of massively multiplayer online games. The objective of the research is to identify and develop general self-adaptive routines and software tools that can be incorporated in a software developer's kit (SDK) that is suitable for licensing to third-party developers. A series of experiments conducted within a statistical framework will identify first- and second-order effects of parameter choices for the evolutionary control of game characters, which will be incorporated into the SDK. R&D will be aimed at generating the most rapid evolutionary learning for game characters while having the smallest code "footprint." Additional research will facilitate automatic play testing and optimization of artificial intelligence in games. The scientific and technical understanding of hybridizing evolutionary computation and neural networks will be enhanced by the careful study of the nonlinear effects of parameter choices in the studied settingsIf successful this product will ease the transition of video games from development to products. The development of an SDK that will help reduce the time and cost of segments of video game production by 50-80%. The software developed may serve as educational classroom aids in university courses. Furthermore, the strong correlation between video games and military simulations suggests important contributions to dynamic planning in combat simulations, as well as extensions to optimizing courses of action in business operations, such as supply-chain management.