Collaborative Learning Agent (CLA)

Summary

A single Collaborative Learning Agent learns and discovers knowledge and behavior patterns from historical data and then applies the patterns for identification of patterns in the new data. The knowledge patterns, discovered automatically using machine learning and pattern recognition methods, include the following patterns 1) Similarity patterns, i.e. group similar data; 2) Correlation patterns, i.e. find hidden relationships among data; 3) Predictive patterns, i.e. make predictions based on historical data; and 4) Recommendation patterns, i.e. make predictions when little or no historical data is available.

A set of networked Collaborative Learning Agents (CLAs) forms an agent network include the following capabilities 1) Text mining: extract concepts and meaning clusters based on contexts; 2) Machine learning: extract knowledge patterns that link meaning to raw text or data observation; and 3) Collaborative meaning search: incorporate human and machine a single loop to form a collaborative network to search and enhance the meaning iteratively.

A text mining technique, Context-Concept-Cluster (CCC) model (US patent 9,323,837), is implemented in the CLAs. The advantage of such a text mining technique over the traditional information retrieval is to capture the cognitive level of understanding of text observations.

Machine learning starts with supervised learning. A train data set with both observations and their labeled meaning are presented to the learning system. The system then generates a correlation model between the categories of observations and meaning labeled by human analysts. In real-life, however, human labeled meaning is expensive to obtain, therefore, it is more important to develop unsupervised learning to achieve the same goal. Here we want to show that CLAs perform an unsupervised learning and categorize a new information into four categories. 1) Anomaly category showing a search input has low correlation with previously discovered context patterns; 2) Relevant category showing an input is highly correlated to the previously discovered knowledge patterns; 3) Low: between relevant and anomaly; 4) Irrelevant category showing an input is not related to any of the agents.