Analysis of the effectiveness of using artificial intelligence to control enemy forces in tower defense games

DOI: 10.31673/2412-9070.2025.018463

Abstract

This research paper is devoted to analyzing the effectiveness of using artificial intelligence to control enemy forces in Tower Defense games. In today's gaming world, Tower Defense games play an important role, and the use of artificial intelligence can greatly improve the gameplay and provide a more refined experience for players.
The topic of this article is devoted to the analysis of the effectiveness of using artificial intelligence to control enemy forces in tower defense games. The study examines the general characterristics and features of artificial intelligence. The results of the analysis show that the existing methods of shaping enemy behavior have both advantages and disadvantages.
A deep analysis of existing methodologies in this field revealed several key problems related to the predictability of enemy artificial intelligence. These problems include frequent errors due to incomplete or inaccurate information, limited opportunities to analyze different decision-making options, and the complexity of managing a large set of potential actions. 
After a detailed review of both the theoretical foundations and empirical studies, it became clear that the application of the fundamental principles of the partial enumeration optimization method (PEOM) has significant potential for further development. The main goal of future research is to improve the current algorithms developed for creating scenarios of the behavior of opponents in turn-based strategy games. This is aimed at eliminating the shortcomings of previous methodologies and improving the overall efficiency and quality of the gaming experience.

Keywords: Artificial Intelligence; Tower Defense; management of enemy forces; machine learning algorithm; Partial Enumeration Optimization Method (PEOM); analysis of the effectiveness of the use of artificial intelligence.