Explainability of AI-Driven Air Combat Agent

Emre Saldiran; Mehmet Hasanzade; Gokhan Inalhan; Antonios Tsourdos

doi:10.1109/CAI54212.2023.00044

Explainability of AI-Driven Air Combat Agent

Emre Saldiran^*
, Mehmet Hasanzade
, Gokhan Inalhan
, Antonios Tsourdos

^*Bu çalışma için yazışmadan sorumlu yazar

Cranfield University

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

2 Atıf (Scopus)

Özet

In safety-critical applications, it is crucial to verify and certify the decisions made by AI-driven Autonomous Systems (ASs). However, the black-box nature of neural networks used in these systems often makes it challenging to achieve this. The explainability of these systems can help with the verification and certification process, which will speed up their deployment in safety-critical applications. This study investigates the explainability of AI-driven air combat agents via semantically grouped reward decomposition. The paper presents two use cases to demonstrate how this approach can help AI and non-AI experts to evaluate and debug the behavior of RL agents.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	85-86
Sayfa sayısı	2
ISBN (Elektronik)	9798350339840
DOI'lar	https://doi.org/10.1109/CAI54212.2023.00044
Yayın durumu	Yayınlandı - 2023
Harici olarak yayınlandı	Evet
Etkinlik	2023 IEEE Conference on Artificial Intelligence, CAI 2023 - Santa Clara, United States Süre: 5 Haz 2023 → 6 Haz 2023

Yayın serisi

Adı	Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	2023 IEEE Conference on Artificial Intelligence, CAI 2023
Ülke/Bölge	United States
Şehir	Santa Clara
Periyot	5/06/23 → 6/06/23

Bibliyografik not

Publisher Copyright:
© 2023 IEEE.

Finansman

This work is funded by BAE Systems.

Finansörler
BAE Systems

Belgeye Erişim

10.1109/CAI54212.2023.00044

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Saldiran, E., Hasanzade, M., Inalhan, G., & Tsourdos, A. (2023). Explainability of AI-Driven Air Combat Agent. In Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023 (pp. 85-86). (Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAI54212.2023.00044

@inproceedings{6fb3ab424f984b40b4170815030563ca,

title = "Explainability of AI-Driven Air Combat Agent",

abstract = "In safety-critical applications, it is crucial to verify and certify the decisions made by AI-driven Autonomous Systems (ASs). However, the black-box nature of neural networks used in these systems often makes it challenging to achieve this. The explainability of these systems can help with the verification and certification process, which will speed up their deployment in safety-critical applications. This study investigates the explainability of AI-driven air combat agents via semantically grouped reward decomposition. The paper presents two use cases to demonstrate how this approach can help AI and non-AI experts to evaluate and debug the behavior of RL agents.",

keywords = "air combat, explainable, reinforcement learning, reward decomposition",

author = "Emre Saldiran and Mehmet Hasanzade and Gokhan Inalhan and Antonios Tsourdos",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Conference on Artificial Intelligence, CAI 2023 ; Conference date: 05-06-2023 Through 06-06-2023",

year = "2023",

doi = "10.1109/CAI54212.2023.00044",

language = "English",

series = "Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "85--86",

booktitle = "Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023",

address = "United States",

}

Saldiran, E, Hasanzade, M, Inalhan, G & Tsourdos, A 2023, Explainability of AI-Driven Air Combat Agent. in Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023. Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023, Institute of Electrical and Electronics Engineers Inc., pp. 85-86, 2023 IEEE Conference on Artificial Intelligence, CAI 2023, Santa Clara, United States, 5/06/23. https://doi.org/10.1109/CAI54212.2023.00044

Explainability of AI-Driven Air Combat Agent. / Saldiran, Emre; Hasanzade, Mehmet; Inalhan, Gokhan et al.
Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 85-86 (Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

TY - GEN

T1 - Explainability of AI-Driven Air Combat Agent

AU - Saldiran, Emre

AU - Hasanzade, Mehmet

AU - Inalhan, Gokhan

AU - Tsourdos, Antonios

PY - 2023

Y1 - 2023

N2 - In safety-critical applications, it is crucial to verify and certify the decisions made by AI-driven Autonomous Systems (ASs). However, the black-box nature of neural networks used in these systems often makes it challenging to achieve this. The explainability of these systems can help with the verification and certification process, which will speed up their deployment in safety-critical applications. This study investigates the explainability of AI-driven air combat agents via semantically grouped reward decomposition. The paper presents two use cases to demonstrate how this approach can help AI and non-AI experts to evaluate and debug the behavior of RL agents.

AB - In safety-critical applications, it is crucial to verify and certify the decisions made by AI-driven Autonomous Systems (ASs). However, the black-box nature of neural networks used in these systems often makes it challenging to achieve this. The explainability of these systems can help with the verification and certification process, which will speed up their deployment in safety-critical applications. This study investigates the explainability of AI-driven air combat agents via semantically grouped reward decomposition. The paper presents two use cases to demonstrate how this approach can help AI and non-AI experts to evaluate and debug the behavior of RL agents.

KW - air combat

KW - explainable

KW - reinforcement learning

KW - reward decomposition

UR - https://www.scopus.com/pages/publications/85168689179

U2 - 10.1109/CAI54212.2023.00044

DO - 10.1109/CAI54212.2023.00044

M3 - Conference contribution

AN - SCOPUS:85168689179

T3 - Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023

SP - 85

EP - 86

BT - Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Conference on Artificial Intelligence, CAI 2023

Y2 - 5 June 2023 through 6 June 2023

ER -

Explainability of AI-Driven Air Combat Agent

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Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

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