  An Artificial Intelligence (AI) system is an autonomous system which emulates
human mental and physical activities such as Observe, Orient, Decide, and Act,
called the OODA process. An AI system performing the OODA process requires a
semantically rich representation to handle a complex real world situation and
ability to reason under uncertainty about the situation. Multi-Entity Bayesian
Networks (MEBNs) combines First-Order Logic with Bayesian Networks for
representing and reasoning about uncertainty in complex, knowledge-rich
domains. MEBN goes beyond standard Bayesian networks to enable reasoning about
an unknown number of entities interacting with each other in various types of
relationships, a key requirement for the OODA process of an AI system. MEBN
models have heretofore been constructed manually by a domain expert. However,
manual MEBN modeling is labor-intensive and insufficiently agile. To address
these problems, an efficient method is needed for MEBN modeling. One of the
methods is to use machine learning to learn a MEBN model in whole or in part
from data. In the era of Big Data, data-rich environments, characterized by
uncertainty and complexity, have become ubiquitous. The larger the data sample
is, the more accurate the results of the machine learning approach can be.
Therefore, machine learning has potential to improve the quality of MEBN models
as well as the effectiveness for MEBN modeling. In this research, we study a
MEBN learning framework to develop a MEBN model from a combination of domain
expert's knowledge and data. To evaluate the MEBN learning framework, we
conduct an experiment to compare the MEBN learning framework and the existing
manual MEBN modeling in terms of development efficiency.
