Delivering on the Promise of Autonomous Agents in the Battlefield

被引:0
作者
Fossaceca, John M. [1 ]
机构
[1] DEVCOM Army Res Lab, 2800 Powder Mill Rd, Adelphi, MD 20783 USA
来源
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR MULTI-DOMAIN OPERATIONS APPLICATIONS V | 2023年 / 12538卷
关键词
Artificial Intelligence; Machine Learning; Autonomy; Behavioral Cloning; Learning from Human Demonstration; Multi-Agent; Reinforcement Learning; Resilience;
D O I
10.1117/12.2663186
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As the Army begins to deliver on the promise of autonomous agents including unmanned ground and air vehicles and other intelligent agents the focus has shifted from functionality to robustness and resilience. Intelligent systems must not only be capable of performing specific tasks, but they must also do it in a manner that is resilient to the dynamism of real-world environments and adapt to the unexpected including adversaries. This paper surveys some of the recent research and breakthroughs towards robust and resilient autonomy, some of the keys to achieving systems that learn rapidly in new environments, and that demonstrate the ability deal with uncertainty through various means including leveraging human knowledge to greatly speed up the learning process.
引用
收藏
页数:10
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