Machine Learning Techniques For Autonomous Agents In Military Simulations - Multum In Parvo
2017 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)(2017)
摘要
In military simulations, software agents are used to represent individuals, weapon platforms or aggregates thereof. Modeling the behavioral capabilities and limitations of such agents may be time-consuming, requiring extensive interaction with subject matter experts and complicated scripts, but nevertheless resulting in rigid, predictable performance. Autonomous agents that learn desired behaviors themselves using Machine Learning (ML) techniques can overcome these shortcomings. However, such techniques are not yet widely used and perhaps underappreciated. In this context, the latin expression "multum in parvo" ("much in little") denotes that ML agents are able to yield a large variety of behavior, despite their compactness in terms of code and usage of physical memory. This paper attempts to provide some background on applicable Machine Learning solutions and their potential military application. The paper is based on the work of the NATO Research Task Group IST121 Machine Learning Techniques for Autonomous Computer Generated Entities.
更多查看译文
关键词
subject matter experts,complicated scripts,rigid performance,predictable performance,autonomous agents,Machine Learning techniques,ML agents,applicable Machine Learning solutions,potential military application,NATO Research Task Group IST-121 Machine,Autonomous Computer Generated Entities,software agents,weapon platforms,behavioral capabilities,time-consuming,extensive interaction,multum in parvo,military simulations,physical memory
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要