SYSTEM FOR EXPERT-ASSISTED CAUSAL INFERENCE FOR RANKING EVENTS OF INTEREST IN NETWORKS

Networks have increased in size and complexity such that the number of events occurring each day has grown drastically. Techniques of this proposal provide for the ability to infer candidates for causal relationships—in some cases, with confidence. In particular, a novel machine learning (ML) based system is described that provides for the ability to narrow-down candidate temporal patterns that may potentially explain an event of interest (e.g., a network outage). The system is trainable with a human in the loop and is highly effective even with minimal amount of prior training

MULTI-ABSTRACTIVE CONTEXT INTERPRETATIONS OF NETWORK EVENTS

Hybrid and augmented workflows involving predictions or insights produced by automation tools that are handed over to human operators are known to cause cognitive overload. Generally, cognitive overload occurs when an automated system tries to push too much information to a human operator. When such a push of information is sustained over time, cognitive overload leads to what is known as alert fatigue whereby insights of an automated system are not utilized, which can lead to poor adoption. One type of cognitive overload specific to cognitive systems includes situations in which predictions/insights are not necessarily numerous but rather too complex understand and interpret. The lack of ability to understand reasons behind predictions can be a barrier to a broader adoption of artificial intelligence (AI) operations. Presented herein is a novel technique to derive explanations for predictions using multiple contexts, which can help system users to rapidly estimate the importance of predictions from several angles, thereby leading to greater trust and system adoption, as well as improved reaction time