Dynamic Weight-Based Pathfinding for Maze Generation Using Randomized Recursive Algorithms

Abstract

These days, Artificial Intelligence (AI) has become more and more popular. Pathfinding AI is one of them. There are many ways to train AI, one of them is letting them solve the maze, that is what this paper is talking about. The main logic is the dynamic weight of the road. Assume there is a robot that connects the start and the end. Wherever it passes, is the path. On each position, the algorithm will generate a number based on random numbers and the distance towards the end. After that, the robot will go to the next road that has the highest weight of road. Then continue generating the weight of the next point. Finally, the author of the paper found the maximum number of movements down needs to be higher otherwise the robot will go close to the edge of the maze. Overall, this study demonstrates how simple dynamic weighting rules can guide effective pathfinding in mazes while revealing the importance of careful parameter tuning.