{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:37:00Z","timestamp":1777390620482,"version":"3.51.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>In modern assembly pipelines, identifying anomalies is crucial in ensuring product quality and operational efficiency. Conventional single-modality methods fail to capture the intricate relationships required for precise anomaly prediction in complex predictive environments with abundant data and multiple modalities. This paper proposes a neurosymbolic AI and fusion-based approach for multimodal anomaly prediction in assembly pipelines. We introduce a time series and image-based fusion model that leverages decision-level fusion techniques. Our research builds upon three primary novel approaches in multimodal learning: time series and image-based decision-level fusion modeling, transfer learning for fusion, and knowledge-infused learning. We evaluate the novel method using our derived and publicly available multimodal dataset and conduct comprehensive ablation studies to assess the impact of our preprocessing techniques and fusion model compared to traditional baselines. The results demonstrate that a neurosymbolic AI-based fusion approach that uses transfer learning can effectively harness the complementary strengths of time series and image data, offering a robust and interpretable approach for anomaly prediction in assembly pipelines with enhanced performance. \\noindent The datasets, codes to reproduce the results, supplementary materials, and demo are available at https:\/\/github.com\/ChathurangiShyalika\/NSF-MAP.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1037","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"9330-9338","source":"Crossref","is-referenced-by-count":2,"title":["NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelines"],"prefix":"10.24963","author":[{"given":"Chathurangi","family":"Shyalika","sequence":"first","affiliation":[{"name":"Artificial Intelligence Institute, University of South Carolina"}]},{"given":"Renjith","family":"Prasad","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Institute, University of South Carolina"}]},{"given":"Fadi","family":"El Kalach","sequence":"additional","affiliation":[{"name":"Department of Automotive Engineering, Clemson University"}]},{"given":"Revathy","family":"Venkataramanan","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Institute, University of South Carolina"}]},{"given":"Ramtin","family":"Zand","sequence":"additional","affiliation":[{"name":"Intelligent Circuits, Architectures and Systems Lab, University of South Carolina"}]},{"given":"Ramy","family":"Harik","sequence":"additional","affiliation":[{"name":"Department of Automotive Engineering, Clemson University"}]},{"given":"Amit","family":"Sheth","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Institute, University of South Carolina"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:35:56Z","timestamp":1758627356000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1037"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1037","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}