Abstract. Bayes’ Theory of conditional probability and efficient algorithms for probability
computation together allow a probabilistic approach to expert systems known
as Bayesian Belief Networks (BBNs). BBNs facilitate the graphical representation
of complex problems and allow users to make expert predictions
on the likelihood of a hypothesis in the absence of
complete information. As such they seem applicable to the problem
solving in the face of uncertainty that characterizes enemy course
of action (COA) assessment at the tactical level of war.
In this paper, BBNs were constructed to reflect two distinct
tactical intelligence problems, one based on conventional operations, and one
based on peace support operations (PSO). Difficulties were encountered in
quantifying the PSO BBN because the intelligence collection plan reflected
a requirement to collect information on enemy capabilities as well
as intent. Consequently, the BBN had to be modified. Overall,
however, it was found that BBNs could be constructed to
reflect the tactical enemy COA assessment problem. Nevertheless, it was
found that the utility of such BBNs was limited, especially
in the conventional environment, because of the likely requirement to
modify quantification to reflect actual battlefield factors such as weather
and terrain, even for the same set of COA. It
was considered that the development of a library of BBN
fragments prior to deployment could go some way to alleviate
the problem, although mainly in the PSO environment with a
slower operational tempo. On the other hand, the modification problem
could be solved by making the BBN more general, allowing
it to be used as a tactical indicator and warning
tool, at least in the PSO environment.
Related topics:
Operations research, simulation and training
View first page of "Brosnan: Use of Bayesian Belief Networks for Enemy Course of Action Assessment at the Tactical Level"
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