The Challenge of Believability in Video Games: Definitions, Agents Models and Imitation Learning

Fabien Tencé (1) , Cédric Buche (1) , Pierre de Loor (1) , Olivier Marc Marc (1)

1 LISYC - Laboratoire d'Informatique des Systèmes Complexes

In this paper, we address the problem of creating believable agents (virtual characters) in video games. We consider only one meaning of believability, ``giving the feeling of being controlled by a player'', and outline the problem of its evaluation. We present several models for agents in games which can produce believable behaviours, both from industry and research. For high level of believability, learning and especially imitation learning seems to be the way to go. We make a quick overview of different approaches to make video games' agents learn from players. To conclude we propose a two-step method to develop new models for believable agents. First we must find the criteria for believability for our application and define an evaluation method. Then the model and the learning algorithm can be designed.

Mots clés: Adaptive decision making; believability; human-like; evaluation; imitation learning; video games.; video games

Domaines: Intelligence artificielle [cs.AI]

Fabien Tencé, Cédric Buche, Pierre de Loor, Olivier Marc Marc. The Challenge of Believability in Video Games: Definitions, Agents Models and Imitation Learning. GAMEON-ASIA'2010, Mar 2010, France. pp.38-45. ⟨hal-00514524⟩