Towards neuro-symbolic reinforcement learning for trustworthy human-autonomy teaming

Priti Gurung; Jiang Li; Danda B. Rawat

doi:10.1117/12.3014232

7 June 2024 Towards neuro-symbolic reinforcement learning for trustworthy human-autonomy teaming

Priti Gurung, Jiang Li, Danda B. Rawat

Proceedings Volume 13054, Assurance and Security for AI-enabled Systems; 1305407 (2024) https://doi.org/10.1117/12.3014232
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Artificial Intelligence (AI) has shown a tremendous impact on civilian and military applications. However, traditional AI will remain inadequate because of issues such as explicit and implicit biases and explainability for operating independently in dynamic and complex environments for the foreseeable future. For human-autonomy teaming (HAT), trustworthy AI is crucial since machines/autonomy and humans work in collaboration for shared learning and joint reasoning for a given mission with combat speed and high accuracy, trust, and assurance. In this paper, we present a brief survey of recent advances, some key challenges, and future research directions for neuro-symbolic-reinforcement-learning-enabled trustworthy HAT.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Priti Gurung, Jiang Li, and Danda B. Rawat "Towards neuro-symbolic reinforcement learning for trustworthy human-autonomy teaming", Proc. SPIE 13054, Assurance and Security for AI-enabled Systems, 1305407 (7 June 2024); https://doi.org/10.1117/12.3014232

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