Modern manufacturing systems demand real-time, trustworthy, and interpretable insights into anomalies and their underlying causes. However, conventional pipelines treat anomaly detection, causal inference, and decision-making as siloed tasks, lacking integration, explainability, and adaptability. We present CausalPulse, an intelligent, multi-agent copilot for automated Root Cause Analysis (RCA) in industrial settings. Built on a modular and extensible architecture, the system leverages standard agentic protocols, including Model Context Protocol (MCP), Agent2Agent (A2A), and LangGraph for dynamic tool and agent discovery and seamless orchestration of tasks. Agents dynamically interact to perform data preprocessing, anomaly detection, causal discovery, and root cause analysis through a neurosymbolic workflow that combines symbolic reasoning with neural methods. Intelligent postprocessing pipelines enable automatic chaining of agent tasks, enhancing contextual awareness and adaptability. CausalPulse is evaluated using both an academic public dataset (i.e., Future Factories) and an industrial proprietary dataset (i.e., Planar Oxygen Sensor Element) and shows that the system outperforms traditional baselines in interpretability, trustworthiness, and operational utility.

Modern manufacturing environments demand not only accurate predictions but also interpretable insights to process anomalies, root causes, and potential interventions. Existing AI systems often function as isolated black boxes, lacking the seamless integration of prediction, explanation, and causal reasoning required for a unified decision-support solution. This fragmentation limits their trustworthiness and practical utility in high-stakes industrial environments. In this work, we present CausalTrace, a neurosymbolic causal analysis module integrated into the SmartPilot industrial CoPilot. CausalTrace performs data-driven causal analysis enriched by industrial ontologies and knowledge graphs, including advanced functions such as causal discovery, counterfactual reasoning, and root cause analysis (RCA). It supports real-time operator interaction and is designed to complement existing agents by offering transparent, explainable decision support. We conducted a comprehensive evaluation of CausalTrace using multiple causal assessment methods and the C3AN framework (i.e. Custom, Compact, Composite AI with Neurosymbolic Integration), which spans principles of robustness, intelligence, and trustworthiness. In an academic rocket assembly testbed, CausalTrace achieved substantial agreement with domain experts (ROUGE-1: 0.91 in ontology QA) and strong RCA performance (MAP@3: 94%, PR@2: 97%, MRR: 0.92, Jaccard: 0.92). It also attained 4.59/5 in the C3AN evaluation, demonstrating precision and reliability for live deployment.

Generating high-quality annotations for object detection and recognition is a challenging and important task, especially in relation to safety-critical applications such as autonomous driving (AD). Due to the difficulty of perception in challenging situations such as occlusion, degraded weather, and sensor failure, objects can go unobserved and unlabeled. In this paper, we present CLUE-AD, a general-purpose method for detecting and labeling unobserved entities by leveraging the object continuity assumption within the context of a scene. This method is dataset-agnostic, supporting any existing and future AD datasets. Using a real-world dataset representing complex urban driving scenes, we demonstrate the applicability of CLUE-AD for detecting unobserved entities and augmenting the scene data with new labels.

Semantic computing technologies have matured to be applicable to many critical domains such as national security, life sciences, and health care. However, the key to their success is the availability of a rich domain knowledge base. The creation and refinement of domain knowledge bases pose difficult challenges. The existing knowledge bases in the health care domain are rich in taxonomic relationships, but they lack nontaxonomic (domain) relationships. In this paper, we describe a semiautomatic technique for enriching existing domain knowledge bases with causal relationships gleaned from Electronic Medical Records (EMR) data. We determine missing causal relationships between domain concepts by validating domain knowledge against EMR data sources and leveraging semantic-based techniques to derive plausible relationships that can rectify knowledge gaps. Our evaluation demonstrates that semantic techniques can be employed to improve the efficiency of knowledge acquisition.

Semantic computing technologies have matured to be applicable to many critical domains, such as life sciences and health care. However, the key to their success is the rich domain knowledge which consists of domain concepts and relationships, whose creation and refinement remains a challenge. In this paper, we develop a technique for enriching domain knowledge, focusing on populating the domain relationships. We determine missing relationships between the domain concepts by validating domain knowledge against real world data sources. We evaluate our approach in the healthcare domain using Electronic Medical Record(EMR) data, and demonstrate that semantic techniques can be used to semi-automate labour intensive tasks without sacrificing fidelity of domain knowledge.

The Internet of Things (IoT) has recently received considerable interest from both academia and industry that are working on technologies to develop the future Internet. It is a joint and complex discipline that requires synergetic efforts from several communities such as telecommunication industry, device manufacturers, semantic Web, and informatics and engineering. Much of the IoT initiative is supported by the capabilities of manufacturing low-cost and energy-efficient hardware for devices with communication capacities, the maturity of wireless sensor network technologies, and the interests in integrating the physical and cyber worlds. However, the heterogeneity of the “Things” makes interoperability among them a challenging problem, which prevents generic solutions from being adopted on a global scale. Furthermore, the volume, velocity and volatility of the IoT data impose significant challenges to existing information systems. Semantic technologies based on machine-interpretable representation formalism have shown promise for describing objects, sharing and integrating information, and inferring new knowledge together with other intelligent processing techniques. However, the dynamic and resource-constrained nature of the IoT requires special design considerations to be taken into account to effectively apply the semantic technologies on the real world data. In this article the authors review some of the recent developments on applying the semantic technologies to IoT.

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations — the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.