Strategy optimization for breakout games based on improved BFS algorithm and image recognition techniques

Abstract

This paper develops a Dungeon Game and details the development process of the City Game. The Dungeon Game is an adventure-themed role-playing game in which the player takes on the role of a character he or she creates and engages in a variety of adventurous activities in a set fictional world. It also allows the use of various props to add interest and realism to the game. In the development of this game, the pathfinding algorithm is crucial to the character's movement and behavior, and the previous traditional pathfinding method can no longer meet the needs of the game, so we optimize the original algorithm, adding dynamic programming and backtracking method in the original algorithm to improve the original algorithm's pathfinding logic to make the character pathfinding in the game more reasonable, to improve the game's performance and user experience. performance and user experience. At the same time, this paper tries to incorporate an image recognition technique based on ANN neural network into the game by adding figures as images into the game, to enrich the content of the game and make it have more possibilities, which increases the playability for the players. In the path optimization test, although the response time is slightly inaccurate, the overall response time is gradually decreasing as the optimization algorithms improve. These algorithm improvements have a more significant increase in the efficiency of the game.