Context-aware Collaborative Neuro-Symbolic Inference in IoBTs

Tarek Abdelzaher, Nathaniel D. Bastian, Susmit Jha, Lance Kaplan, Mani Srivastava, Venugopal V. Veeravalli

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationMILCOM 2022 - 2022 IEEE Military Communications Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665485340
StatePublished - 2022
Event2022 IEEE Military Communications Conference, MILCOM 2022 - Rockville, United States
Duration: Nov 28 2022Dec 2 2022

Publication series

NameProceedings - IEEE Military Communications Conference MILCOM


Conference2022 IEEE Military Communications Conference, MILCOM 2022
Country/TerritoryUnited States


  • Neuro-symbolic inference
  • robust learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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