Game Development Reference
In-Depth Information
Rule evaluation involves substituting the degrees of membership into the
rules given. For example, if the value for health is good was 17% and the value
for shield is adequate was 25%, Rule 1 would become:
if 17% and 25% then mood is (17%) happy
Only the part of the rule in the if statement (shown in bold) has the fuzzy
values substituted. If the values are connected by an AND, the smallest degree
of membership is assigned to the then part. If the values are connected by an
OR, the larger degree of membership is assigned. Assuming health is bad is
50% and shield is moderate is 25%, Rule 2 would become:
if 50% or 25% then mood is (50%) indifferent
These values are then used to clip the fuzzy sets for happy and indifferent
pertaining to mood. In brief, 17% of the happy set and 50% of the indifferent
set are merged to create a new fuzzy set as shown in Figure 5.17 . This is the
third step of Rule Aggregation.
The final step is defuzzification. This takes the final aggregated fuzzy set
and converts it into a single value that will be the fuzzy output. The final
fuzzy set contains the value; all that is needed is to extract it. The simplest
method for doing this is called the centroid technique, which finds the
mathematical center of gravity of the set. This involves multiplying all the
values in the set with their degree of membership, adding them together,
and dividing by the sum of all the degrees of membership in the set. This
gives a percentage value, in this case, pertaining to the mood value. For
example, the result might be a 40% mood that would result in the mood
being indifferent .
Developing a fuzzy logic engine requires a great deal of understanding
in programming and mathematics. There is certainly not enough room in
this chapter to provide a full example. Therefore, the next workshop uses
an Open Source fuzzy logic engine called DotFuzzy (available from http://
havana7.com/dotfuzzy/ ) written in C#.
FIG 5.17 Merged fuzzy sets.
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