Learning Agent (CLA)
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.
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.
text mining technique, Context-Concept-Cluster (CCC) model (US Patent
Pending), 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.
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.
advantage of using machine learning is for automation and reducing
manpower. For example, to prevent potential threats and events of
terrorism related to the global maritime domain, DOD, federal, state and
local agencies have to work together to make situation awareness
effectively based on a Common Operational Picture (COP). In this
environment there are numerous trouble reports.
These trouble reports are a usually assessed, analyzed and
resolved by human analysts. Agent machine learning provides a mechanism
to transfer knowledge patterns of human analysts to computer agents.
Therefore manpower required for handling the trouble reports can be
Trident Warrior ’08, which is an annual
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