6. The method of claim 5 , further comprising: f) determining whether a difference between the behavior combinations having one of the averages of the probability of the feeling expression behaviors closer than the optimal behavior combination to the predetermined feeling values of the machine and the optimal behavior combination satisfies a randomness; g) when the randomness is satisfied, substituting the optimal behavior combination with the behavior combinations having one of the averages of the probability of the feeling expression behaviors closer than the optimal behavior combination to the predetermined feeling values of the machine, and subtracting the repeated value, and when the repeated value is smaller than the predetermined value, selecting the optimal behavior combination as the motion of the machine and otherwise, returning to the step c); and h) when the randomness is not satisfied, returning to the step c).
6. The method of claim 5 , further comprising: f) determining whether a difference between the behavior combinations having one of the averages of the probability of the feeling expression behaviors closer than the optimal behavior combination to the predetermined feeling values of the machine and the optimal behavior combination satisfies a randomness; g) when the randomness is satisfied, substituting the optimal behavior combination with the behavior combinations having one of the averages of the probability of the feeling expression behaviors closer than the optimal behavior combination to the predetermined feeling values of the machine, and subtracting the repeated value, and when the repeated value is smaller than the predetermined value, selecting the optimal behavior combination as the motion of the machine and otherwise, returning to the step c); and h) when the randomness is not satisfied, returning to the step c). 8. The method of claim 6 , determining whether the randomness is satisfied is determined according to the following Equation: 
 P A ( k )>random(0,1), wherein: P A ⁡ ( k ) = ⅇ 1 Z ⁢ ( error ⁡ ( b est ⁡ ( k ) ) - error ⁡ ( b can ⁡ ( k ) ) ) ; random (0, 1) denotes a random value between 0 and 1; Z denotes an ending constant having a value reduced whenever the repeated value is counted; error (b best (k)) denotes a size of a difference between the optimal behavior combination and the predetermined feeling values of the machine; and error (b can (k)) denotes a size of a difference between the behavior combinations and the predetermined feeling values of the machine and is defined by the following equation, error ⁡ ( b can ⁡ ( k ) ) =  e ⁡ ( k ) - 1 P ⁢ ∑ p = 1 P ⁢ e _ p , w p  , and wherein: e(k) denotes a feeling value (vector) of the machine to express at a given k time; P denotes a total number of the expression elements of the machine; ē p,w p denotes a feeling value (vector) of a w p -th feeling expression behavior of a p-th expression element; and ∥ ∥ denotes a size of a vector (inter-vector distance).