TY - GEN
T1 - Context-aware Collaborative Neuro-Symbolic Inference in IoBTs
AU - Abdelzaher, Tarek
AU - Bastian, Nathaniel D.
AU - Jha, Susmit
AU - Kaplan, Lance
AU - Srivastava, Mani
AU - Veeravalli, Venugopal V.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features integrated neuro-symbolic inference, where symbolic context is used by deep learning, and deep learning models provide atomic concepts for symbolic reasoning. The incorporation of high-level symbolic reasoning improves data efficiency during training and makes inference more robust, interpretable, and resource-efficient. In this paper, we identify the key challenges in developing context-aware collaborative neuro-symbolic inference in IoBTs and review some recent progress in addressing these gaps.
AB - IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features integrated neuro-symbolic inference, where symbolic context is used by deep learning, and deep learning models provide atomic concepts for symbolic reasoning. The incorporation of high-level symbolic reasoning improves data efficiency during training and makes inference more robust, interpretable, and resource-efficient. In this paper, we identify the key challenges in developing context-aware collaborative neuro-symbolic inference in IoBTs and review some recent progress in addressing these gaps.
KW - Neuro-symbolic inference
KW - robust learning
UR - http://www.scopus.com/inward/record.url?scp=85147328720&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85147328720&partnerID=8YFLogxK
U2 - 10.1109/MILCOM55135.2022.10017607
DO - 10.1109/MILCOM55135.2022.10017607
M3 - Conference contribution
AN - SCOPUS:85147328720
T3 - Proceedings - IEEE Military Communications Conference MILCOM
SP - 1053
EP - 1058
BT - MILCOM 2022 - 2022 IEEE Military Communications Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Military Communications Conference, MILCOM 2022
Y2 - 28 November 2022 through 2 December 2022
ER -