Evolutionary entertainment with intelligent agents

A common limitation of conventional video games is that players learn the positions and behaviour of the computer controlled characters. Software developers preprogram these characteristics so, after playing the game several times, the player comes to know exactly how and when the monsters will act. The game eventually becomes boring because the player need only execute a learned script to defeat the monsters and overcome all the hurdles he or she faces. This pattern has sparked increasing interest in using computational intelligence techniques to control the actions of computer characters, rather than relying on simple heuristics or rule-based systems. In particular, evolutionary computation and neural networks are being adapted to let software characters learn from their own experience, predict what a player might do next, and take appropriate action to meet their own challenges. In this way, a game can remain perpetually novel, posing new tests for the player each time he or she plays. The evolutionary technology we have developed can be used to control characters in intelligent board games and other software entertainment products. Efforts are under way to incorporate self-learning techniques in other games, such as chess, and may provide superior methods for designing continually novel games in the future.