Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

invite the experts to attend a meeting and record the resulting outcome of
the meeting. Alternatively, she may visit the office of the experts and record
his view about the concerned problem and finally combine their views
together by some principle. The former scheme suffers from the following
limitations [6], [13]:
a) Less participation of an expert because of dominance of his
supervisor or senior experts.
b) Compromising solutions generated by a group with conflicting
opinions.
c) Wastage of time in group meetings.
d) Difficulties in scheduling the experts.
All the above limitations of the former scheme, however, can be
overcome by the latter scheme. But how can we integrate the views of the
multiple number of experts in the latter scheme? An answer to this question
is presented here following Rush and Wallace [17]. In a recent publication,
Rush and Wallace devised a scheme to combine ‘the influence diagrams’
(ID) of several experts for constructing a ‘multiple expert influence
diagram’ (MEID). The MEIDs represent the causal dependence
relationship of facts, supported by a majority of the experts, and thus may be
used as a collective source of knowledge, free from human biases and
inconsistencies.
20.3.1 Constructing MEIDs from IDs
Formally, “an ID is a directed graph which displays the decision points,
relevant events and potential outcomes of a decision situation [17].” The
rectangular nodes in an ID represent facts or decisions, while the directed
arcs denote causal dependencies. For understanding the definition, let us
consider the “oil wildcatter decision problem [17]”. Here the decision is
whether to drill at a given geographical location for possible exploration of
natural oil. An ID for the above problem, provided by an expert, is
presented in fig. 20.1.
The IDs can be represented by incidence matrix with (i, j)-th element
denoted by wij, where