UEB ENIB Laboratory STICC

France

UEB ENIB Laboratory STICC

France

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Tence F.,VIRTUALYS | Gaubert L.,UEB ENIB Laboratory STICC | Soler J.,UEB ENIB Laboratory STICC | De Loor P.,UEB ENIB Laboratory STICC | Buche C.,UEB ENIB Laboratory STICC
Computer Animation and Virtual Worlds | Year: 2013

In some video games, humans and computer programs can play together, each one controlling a virtual humanoid. These computer programs usually aim at replacing missing human players; however, they partially miss their goal, as they can be easily spotted by players as being artificial. Our objective is to find a method to create programs whose behaviors cannot be told apart from players when observed playing the game. We call this kind of behavior a believable behavior. To achieve this goal, we choose models using Markov chains to generate the behaviors by imitation. Such models use probability distributions to find which decision to choose depending on the perceptions of the virtual humanoid. Then, actions are chosen depending on the perceptions and the decision. We propose a new model, called Chameleon, to enhance expressiveness and the associated imitation learning algorithm. We first organize the sensors and motors by semantic refinement and add a focus mechanism in order to improve the believability. Then, we integrate an algorithm to learn the topology of the environment that tries to best represent the use of the environment by the players. Finally, we propose an algorithm to learn parameters of the decision model. Copyright © 2013 John Wiley & Sons, Ltd.


Buche C.,UEB ENIB Laboratory STICC | De Loor P.,UEB ENIB Laboratory STICC
Computer Animation and Virtual Worlds | Year: 2013

To be believable, virtual entities must be equipped with the ability to anticipate, that is, to predict the behavior of other entities and the subsequent consequences on the environment. For that purpose, we propose an original approach where the entity possesses an autonomous world of simulation within simulation, in which it can simulate itself (with its own model of behavior) and simulate the environment (with the representation of the behaviors of the other entities). This principle is illustrated by the development of an artificial juggler in 3D. In this application, the juggler predicts the motion of the balls in the air and uses its predictions to coordinate its own behavior to continue to juggle.Copyright © 2012 John Wiley & Sons, Ltd.


Tence F.,UEB ENIB Laboratory STICC | Gaubert L.,UEB ENIB Laboratory STICC | De Loor P.,UEB ENIB Laboratory STICC | Buche C.,UEB ENIB Laboratory STICC
13th International Conference on Intelligent Games and Simulation, GAME-ON 2012 | Year: 2012

The believability of a virtual world can be increased by improving the behavior of the characters in it. Considering 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 depending 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 believability. To achieve believability, we integrate an algorithm to learn the topology of the environment. Then, we revamp the learning algorithm to be able to learn much more parameters and with greater precision at the cost of its time of convergence. © 2012 EUROSIS-ETI.

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