| Title | Context-aware Collaborative Neuro-Symbolic Inference in IoBTs |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Abdelzaher, Tarek, Bastian, Nathaniel D., Jha, Susmit, Kaplan, Lance, Srivastava, Mani, Veeravalli, Venugopal V. |
| Conference Name | MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM) |
| Keywords | Collaboration, Data models, decision making, Deep Learning, human factors, iobt, Neuro-symbolic inference, Perturbation methods, pubcrawl, resilience, Resiliency, Robust Learning, Scalability, surveillance, Training |
| Abstract | 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. |
| DOI | 10.1109/MILCOM55135.2022.10017607 |
| Citation Key | abdelzaher_context-aware_2022 |
Context-aware Collaborative Neuro-Symbolic Inference in IoBTs