Arcade machines have long incorporated artificial intelligence to create engaging and challenging non-player character behavior, though the complexity has evolved significantly over time. Early arcade games used relatively simple AI techniques due to hardware limitations. Finite state machines were commonly employed, where NPCs would transition between predetermined states like "patrol," "attack," or "flee" based on player proximity or game conditions. Games like Pac-Man used pattern-based movement with some random elements to create the illusion of intelligent pursuit.

Pathfinding algorithms such as A* allowed NPCs to navigate maze-like environments efficiently, while decision trees enabled more structured behavioral choices. As arcade hardware advanced, more sophisticated techniques emerged, including rule-based systems where developers would encode "if-then" rules governing NPC reactions to various stimuli.

Modern arcade machines sometimes incorporate machine learning elements, though traditional techniques remain prevalent due to their predictability and performance efficiency. The implementation varies significantly between game genres - fighting games employ behavior patterns and reaction timing, while shooting games use spawn patterns and targeting algorithms. These AI systems create the challenge progression essential to the arcade experience, balancing difficulty to encourage continued play without frustrating players excessively. The limited computational resources in arcade cabinets have historically driven innovative approaches to creating compelling NPC behavior within tight constraints.