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

TL;DR

This paper explores the challenge of creating believable virtual agents in video games, focusing on defining believability, evaluating it, and proposing a two-step method for developing agents using imitation learning.

Contribution

It provides a comprehensive overview of models for believable agents and introduces a structured approach to designing agents with high believability through criteria and evaluation methods.

Findings

Imitation learning is promising for believable agent behavior.

A two-step method for developing believable agents is proposed.

Evaluation criteria are essential for assessing believability.

Abstract

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.