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Nowadays, the diversity and expansions of problems in various branches of science has greatly increased and finding the answer to such problems in a short period of time is considered as a very essential challenge. Using artificial intelligence can significantly accelerate the solving process for complex problems and considerably reduce the response time. The pursuit-evasion problem is one of the problems that can have a high level of complexity due to the nature of the factors involved. Factors causing complexity include the number of factors involved, the range of member’s vision and barriers exist on the playground. Various algorithms have been proposed to solve the pursuit-evasion problem so far, each has its own strengths and weaknesses. In this paper, the pursuit-evasion game is examined by using the linguistic geometry algorithm specifically in a problem with 9×9 playground dimensions and to examine the generalization of the algorithm's performance in problems with different dimensions. It has been shown that this approach can improve the response speed by more than 90%. In this work, the factors affecting the realism of the pursuit-evasion game are examined more carefully in linguistic geometry and finally the problem is solved by simplifying the problem space to a number of subspaces in which the movement paths of each element of the game are clear. We show that, despite the problem space became more complex, the linguistic geometry algorithm creates about 91% improvement over the other algorithms.

The minimax algorithm, also called the negamax algorithm, remains today the most widely used search technique for two-player perfect-information games. However, minimaxing has been shown to be susceptible to game tree pathology, a paradoxical situation in which the accuracy of the search can decrease as the height of the tree increases. Althöfer’s alternative minimax algorithm has been proven to be invulnerable to pathology. However, it has not been clear whether alpha-beta pruning, a crucial component of practical game programs, could be applied in the context of Alhöfer’s algorithm. In this brief paper, we show how alpha-beta pruning can be adapted to Althöfer’s algorithm.