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	    'service=poor      & food=good'      => 'tip=poor',
	    'service=good      & food=good'      => 'tip=good',
	    'service=excellent & food=good'      => 'tip=good',
	    'service=amazing   & food=good'      => 'tip=excellent',

	    'service=poor      & food=excellent' => 'tip=good',
	    'service=good      & food=excellent' => 'tip=excellent',
	    'service=excellent & food=excellent' => 'tip=excellent',
	    'service=amazing   & food=excellent' => 'tip=amazing',

	    'service=poor      & food=amazing'   => 'tip=good',
	    'service=good      & food=amazing'   => 'tip=excellent',
	    'service=excellent & food=amazing'   => 'tip=amazing',
	    'service=amazing   & food=amazing'   => 'tip=amazing',

	    );

    $s->compute(service => 2,
	    food    => 7);

=head1 DESCRIPTION

This module implements a fuzzy inference system. Very briefly, an FIS
is a system defined by a set of input and output variables, and a set
of fuzzy rules relating the input variables to the output variables.
Given crisp values for the input variables, the FIS uses the fuzzy rules
to compute a crisp value for each of the output variables.

The operation of an FIS is split into 4 distinct parts: I<fuzzification>,
I<inference>, I<aggregation> and I<defuzzification>.

=head2 Fuzzification

In this step, the crisp values of the input variables are used to
compute a degree of membership of each of the input variables in each
of its term sets. This produces a set of fuzzy sets.

=head2 Inference

In this step, all the defined rules are examined. Each rule has two parts:
the I<precedent> and the I<consequent>. The degree of support for each
rule is computed by applying fuzzy operators (I<and>, I<or>) to combine
all parts of its precendent, and generate a single crisp value. This value
indicates the "strength of firing" of the rule, and is used to reshape
(I<implicate>) the consequent part of the rule, generating modified
fuzzy sets.

=head2 Aggregation

Here, all implicated fuzzy sets of the fired rules are combined using
fuzzy operators to generate a single fuzzy set for each of the
output variables.

=head2 Defuzzification

Finally, a defuzzification operator is applied to the aggregated fuzzy
set to generate a single crisp value for each of the output variables.

For a more detailed explanation of fuzzy inference, you can check out
the tutorial by Jerry Mendel at
S<http://sipi.usc.edu/~mendel/publications/FLS_Engr_Tutorial_Errata.pdf>.

Note: The terminology used in this module might differ from that used
in the above tutorial.

=head1 PUBLIC METHODS

The module has the following public methods:

=over 4

=item new()

This is the constructor. It takes no arguments, and returns an
initialized AI::FuzzyInference object.

=item operation()

This method is used to set/query the fuzzy operations. It takes at least
one argument, and at most 2. The first argument specifies the logic
operation in question, and can be either C<&> for logical I<AND>,
C<|> for logical I<OR>, or C<!> for logical I<NOT>. The second
argument is used to set what method to use for the given operator.
The following values are possible:

=item &

=over 8

=item min

The result of C<A and B> is C<min(A, B)>. This is the default.

=item product

The result of C<A and B> is C<A * B>.

=back

=item |

=over 8

=item max

The result of C<A or B> is C<max(A, B)>. This is the default.

=item sum

The result of C<A or B> is C<min(A + B, 1)>.

=back

=item !

=over 8

=item complement

The result of C<not A> is C<1 - A>. This is the default.