Implementation of a Pathfinding-Based Intelligent Agent for Enemy Behavior in the "Run to BIPOL" Maze Tournament Game
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Abstract
This study aims to implement a pathfinding-based intelligent agent for autonomous enemy behavior in the web-based maze tournament game Run to BIPOL. The game features a competitive tournament in which one player competes against six AI-controlled opponents to reach limited objectives in procedurally generated maze environments. Development followed the Multimedia Development Life Cycle (MDLC), consisting of Concept, Design, Material Collecting, Assembly, Testing, and Distribution. The intelligent agent integrates Procedural Content Generation (PCG) for maze generation, Breadth-First Search (BFS) for accessibility validation, A* with the Manhattan Distance heuristic for pathfinding, and a Finite State Machine (FSM) for behavioral control. Black-box testing was conducted using Win and Lose scenarios to evaluate the functionality of the developed system. The results showed that the game components and intelligent agent operated successfully under both scenarios. The Win scenario recorded 123 A* executions, an average path length of 10.3 nodes, and 3,256 explored nodes, while the Lose scenario recorded 128 A* executions, an average path length of 8.5 nodes, and 2,156 explored nodes. These results demonstrate that the proposed intelligent agent can autonomously navigate dynamic maze environments and support competitive enemy behavior across multiple tournament stages. The study provides an implementation foundation for intelligent agents in browser-based competitive maze games.
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