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

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

Automatable Evaluation Method Oriented toward Behaviour Believability for Video Games

International audienceClassic evaluation methods of believable agents are time-consuming because they involve many human to judge agents. They are well suited to validate work on new believable behaviours models. However, during the implementation, numerous experiments can help to improve agents' believability. We propose a method which aim at assessing how much an agent's behaviour looks like humans' behaviours. By representing behaviours with vectors, we can store data computed for humans and then evaluate as many agents as needed without further need of humans. We present a test experiment which shows that even a simple evaluation following our method can reveal differences between quite believable agents and humans. This method seems promising although, as shown in our experiment, results' analysis can be difficult

Learning a Representation of a Believable Virtual Character's Environment with an Imitation Algorithm

In video games, virtual characters' decision systems often use a simplified representation of the world. To increase both their autonomy and believability we want those characters to be able to learn this representation from human players. We propose to use a model called growing neural gas to learn by imitation the topology of the environment. The implementation of the model, the modifications and the parameters we used are detailed. Then, the quality of the learned representations and their evolution during the learning are studied using different measures. Improvements for the growing neural gas to give more information to the character's model are given in the conclusion

CHAMELEON: A Learning Virtual Bot For Believable Behaviors In Video Game

International audienceThe believability of a virtual world can be increased by improving the behavior of the characters in it. Consid- ering literature, we choose a model developed by Le Hy to generate the behaviors by imitation. The model uses probability distributions to find which decision to choose depending on the sensors. Then actions are chosen de- pending on the sensors and the decision. The core idea of the model is promising but we propose to enhance the expressiveness of the model and the associated learning algorithm. We hope the model will be able to generate more believable behaviors and learn them with minimal a priori knowledge. We first revamp the organization of the sensors and motors by semantic refinement and add a focus mechanism in order to improve the believabil- ity. To achieve believability, we integrate an algorithm to learn the topology of the environment. Then, we re- vamp the learning algorithm to be able to learn much more parameters and with greater precision at the cost of its time of convergence

Modèle probabiliste de comportement et algorithme d'apprentissage par imitation pour les personnages crédibles dans les jeux vidéo

This manuscript aims at designing a behaviour model for the control of believable characters in video games. We define a believable character as a computer program able to control a virtual body in a virtual environment so that other human users in the environment think the virtual body is controlled by another human user. To be more precise, we define 10 requirements for a character to be believable, based on previous experiments and work. In order to fulfil these requirements, we studied the behaviour models developed both in the research and the industry. As one of the requirements is that the model is able to evolve, we had to find learning algorithms for the behaviour model. We find out that imitation is the best way to believability. With these studies in mind we find out that the behaviour model developed by Le Hy in his thesis answers to most of the requirements but has still some limitations. In this manuscript we use an approach like Le Hy's. We first try to reduce the number of parameters in the model. Then we replace the two mechanisms to break the complexity of the probability distributions by an attention selection mechanism. We add to the model the ability to learn by imitation the layout of environments. Finally we totally revamp the learning algorithm. The proposition makes the model able to learn how to act in the environment rapidly. Stimulus-action associations are made which the agent look-like a human player. However the learning also learns wrong associations which destroy the illusion of believability. According to our studies, our model performs better than Le Hy's but work has still to be done on the model to achieve the final goal.Ce manuscrit cherche à concevoir un modèle de comportement pour le contrôle de personnages crédibles dans les jeux vidéo. Nous définissons un personnage crédible comme un programme informatique capable de contrôler une représentation virtuelle de façon à ce que des observateurs dans l'environnement virtuel pensent que la représentation est contrôlée par un humain. Nous établissons 10 critères plus précis pour établir notre thèse. Pour répondre à ces 10 critères nous avons étudié des modèles développés à la fois dans le domaine académique et de l'industrie. L'évolution étant un des critères, nous avons aussi étudié les algorithmes d'apprentissages existants, notamment ceux basés sur l'imitation étant le mieux adaptés à la crédibilité. De ces études nous avons conclu que le modèle de Le Hy était une excellente base pour de futurs développements. Nous utilisons l'approche de Le Hy mais nous avons effectué des choix différents en vue d'une meilleur crédibilité. Nous proposons un raffinement sémantique et un mécanisme d'attention pour réduire le nombre de paramètres dans le modèle et améliorer le comportement. Un algorithme est ajouté pour permettre au personnage de s'orienter dans l'environnement et un l'algorithme d'apprentissage des paramètres du modèles a été repensé. Ces propositions permettent au modèle d'apprendre rapidement des associations stimuli-actions qui ressemblent à des comportement humains. Cependant de mauvaise associations sont aussi faites rendant le comportement non crédible. Selon nos mesures, notre modèle donne de meilleurs résultats en terme de crédibilité que le modèle de Le Hy, mais des améliorations restent encore à faire pour atteindre notre objectif

Data Clustering and Similarity

International audienc

CHAMELEON: Online Learning for Believable Behaviors based on Humans Imitation in Computer Games.

International audienc