Scalable Interactive Machine Learning for Future Command and Control
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866916181546369024 |
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| author | Madison, Anna Novoseller, Ellen Goecks, Vinicius G. Files, Benjamin T. Waytowich, Nicholas Yu, Alfred Lawhern, Vernon J. Thurman, Steven Kelshaw, Christopher McDowell, Kaleb |
| author_facet | Madison, Anna Novoseller, Ellen Goecks, Vinicius G. Files, Benjamin T. Waytowich, Nicholas Yu, Alfred Lawhern, Vernon J. Thurman, Steven Kelshaw, Christopher McDowell, Kaleb |
| contents | Future warfare will require Command and Control (C2) personnel to make decisions at shrinking timescales in complex and potentially ill-defined situations. Given the need for robust decision-making processes and decision-support tools, integration of artificial and human intelligence holds the potential to revolutionize the C2 operations process to ensure adaptability and efficiency in rapidly changing operational environments. We propose to leverage recent promising breakthroughs in interactive machine learning, in which humans can cooperate with machine learning algorithms to guide machine learning algorithm behavior. This paper identifies several gaps in state-of-the-art science and technology that future work should address to extend these approaches to function in complex C2 contexts. In particular, we describe three research focus areas that together, aim to enable scalable interactive machine learning (SIML): 1) developing human-AI interaction algorithms to enable planning in complex, dynamic situations; 2) fostering resilient human-AI teams through optimizing roles, configurations, and trust; and 3) scaling algorithms and human-AI teams for flexibility across a range of potential contexts and situations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06501 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scalable Interactive Machine Learning for Future Command and Control Madison, Anna Novoseller, Ellen Goecks, Vinicius G. Files, Benjamin T. Waytowich, Nicholas Yu, Alfred Lawhern, Vernon J. Thurman, Steven Kelshaw, Christopher McDowell, Kaleb Machine Learning Artificial Intelligence Computation and Language Human-Computer Interaction I.2.6; I.2.7; J.7 Future warfare will require Command and Control (C2) personnel to make decisions at shrinking timescales in complex and potentially ill-defined situations. Given the need for robust decision-making processes and decision-support tools, integration of artificial and human intelligence holds the potential to revolutionize the C2 operations process to ensure adaptability and efficiency in rapidly changing operational environments. We propose to leverage recent promising breakthroughs in interactive machine learning, in which humans can cooperate with machine learning algorithms to guide machine learning algorithm behavior. This paper identifies several gaps in state-of-the-art science and technology that future work should address to extend these approaches to function in complex C2 contexts. In particular, we describe three research focus areas that together, aim to enable scalable interactive machine learning (SIML): 1) developing human-AI interaction algorithms to enable planning in complex, dynamic situations; 2) fostering resilient human-AI teams through optimizing roles, configurations, and trust; and 3) scaling algorithms and human-AI teams for flexibility across a range of potential contexts and situations. |
| title | Scalable Interactive Machine Learning for Future Command and Control |
| topic | Machine Learning Artificial Intelligence Computation and Language Human-Computer Interaction I.2.6; I.2.7; J.7 |
| url | https://arxiv.org/abs/2402.06501 |