5 papers · 1 filter
CausalPulse: An Industrial-Grade Neurosymbolic Multi-Agent Copilot for Causal Diagnostics in Smart Manufacturing
Chathurangi Shyalika, Utkarshani Jaimini, Cory Henson +1
Modern manufacturing environments demand real-time, trustworthy, and interpretable root-cause insights to sustain productivity and quality. Traditional analytics pipelines often tr…
CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturing
Chathurangi Shyalika, Aryaman Sharma, Fadi El Kalach +4
Modern manufacturing environments demand not only accurate predictions but also interpretable insights to process anomalies, root causes, and potential interventions. Existing AI s…
Influence of Backdoor Paths on Causal Link Prediction
Utkarshani Jaimini, Cory Henson, Amit Sheth
The current method for predicting causal links in knowledge graphs uses weighted causal relations. For a given link between cause-effect entities, the presence of a confounder affe…
HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph
Utkarshani Jaimini, Cory Henson, Amit Sheth
Causal networks are often incomplete with missing causal links. This is due to various issues, such as missing observation data. Recent approaches to the issue of incomplete causal…
CausalLP: Learning causal relations with weighted knowledge graph link prediction
Utkarshani Jaimini, Cory Henson, Amit P. Sheth
Causal networks are useful in a wide variety of applications, from medical diagnosis to root-cause analysis in manufacturing. In practice, however, causal networks are often incomp…