  In many complex, real-world situations, problem solving and decision making
require effective reasoning about causation and uncertainty. However, human
reasoning in these cases is prone to confusion and error. Bayesian networks
(BNs) are an artificial intelligence technology that models uncertain
situations, supporting probabilistic and causal reasoning and decision making.
However, to date, BN methodologies and software require significant upfront
training, do not provide much guidance on the model building process, and do
not support collaboratively building BNs. BARD (Bayesian ARgumentation via
Delphi) is both a methodology and an expert system that utilises (1) BNs as the
underlying structured representations for better argument analysis, (2) a
multi-user web-based software platform and Delphi-style social processes to
assist with collaboration, and (3) short, high-quality e-courses on demand, a
highly structured process to guide BN construction, and a variety of helpful
tools to assist in building and reasoning with BNs, including an automated
explanation tool to assist effective report writing. The result is an
end-to-end online platform, with associated online training, for groups without
prior BN expertise to understand and analyse a problem, build a model of its
underlying probabilistic causal structure, validate and reason with the causal
model, and use it to produce a written analytic report. Initial experimental
results demonstrate that BARD aids in problem solving, reasoning and
collaboration.
