Abstract: So far the modified simplex method and simplex method have been used for the solution of the linear goal programming and linear programming respectively. Of course, these methods are effective and successful, but they are some what trouble and time-consuming when the scale of problem is larger. Recently author discovered that and artificial intelligence-algebraic algorithm can be used for solving this two kinds of problem. The main idea of this algorithm is that based on the practical background of the problem man's wisdom is used to analyse which of the inequality constraints should be equalties to make the optimized goal function or the objective function optimum. Asuming that there are $m'$ of equalities in $m$ inequality constraints, in which only $n$ decision variables are ncluded and so there are $n-m'$ of decision variables should be zeros to make the optimized goal function or the objective function optimum. The optimality condition is used to determine which of decision variables equal to zeros. At last, the optimum solution or the satistactory solution can be solved from the $m'$ equality equations including $m'$ decision variables. Many examples we collected show that this algorithm is very simple, rapid and effective and the results obtained by this algorithm and by the traditional simplex method are almost all consistent, exception of a few examples due to mistakes happened in the calculation of simplex methods. And so this algorithm posseses considerably wide applicability. But the universal applicability didn't be proved theretically yet. Therefore an important aim of this paper is to attract readers to investigate with us the problem of whether this algorithm posseses universal applicability.