Game AI revisited

Abstract

More than a decade after the early research efforts on the use of artificial intelligence (AI) in computer games and the establishment of a new AI domain the term “game AI” needs to be redefined. Traditionally, the tasks associated with

game AI revolved around non player character (NPC) behavior at different levels of control, varying from navigation

and pathfinding to decision making. Commercial-standard games developed over the last 15 years and current game productions, however, suggest that the traditional challenges

of game AI have been well addressed via the use of sophisticated AI approaches, not necessarily following or inspired

by advances in academic practices. The marginal penetration of traditional academic game AI methods in industrial

productions has been mainly due to the lack of constructive communication between academia and industry in the

early days of academic game AI, and the inability of academic game AI to propose methods that would significantly

advance existing development processes or provide scalable solutions to real world problems. Recently, however, there has been a shift of research focus as the current plethora of AI uses in games is breaking the non-player character AI

tradition. A number of those alternative AI uses have already shown a significant potential for the design of better

games. This paper presents four key game AI research areas that are currently reshaping the research roadmap in the game AI field and evidently put the game AI term under a new perspective. These game AI flagship research areas include

the computational modeling of player experience, the procedural generation of content, the mining of player data on

massive-scale and the alternative AI research foci for enhancing NPC capabilities.