Towards A Theory-Of-Mind-Inspired Generic Decision-Making Framework

Simulation is widely used to make model-based predictions, but few approaches have attempted this technique in dynamic physical environments of medium to high complexity or in general contexts. After an introduction to the cognitive science concepts from which this work is inspired and the current development in the use of simulation as a decision-making technique, we propose a generic framework based on theory of mind, which allows an agent to reason and perform actions using multiple simulations of automatically created or externally inputted models of the perceived environment. A description of a partial implementation is given, which aims to solve a popular game within the IJCAI2013 AIBirds contest. Results of our approach are presented, in comparison with the competition benchmark. Finally, future developments regarding the framework are discussed.Comment: 7 pages, 5 figures, IJCAI 2013 Symposium on AI in Angry Bird

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

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

Conditional autoencoder pre-training and optimization algorithms for personalized care of hemophiliac patients

This paper presents the use of deep conditional autoencoder to predict the effect of treatments for patients suffering from hemophiliac disorders. Conditional autoencoder is a semi-supervised model that learns an abstract representation of the data and provides conditional reconstruction capabilities. Such models are suited to problems with limited and/or partially observable data, common situation for data in medicine. Deep conditional autoencoders allow the representation of highly non-linear functions which makes them promising candidates. However, the optimization of parameters and hyperparameters is particularly complex. For parameter optimization, the classical approach of random initialization of weight matrices works well in the case of simple architectures, but is not feasible for deep architectures. For hyperparameter optimization of deep architectures, the classical cross-validation method is costly. In this article, we propose solutions using a conditional pre-training algorithm and incremental optimization strategies. Such solutions reduce the variance of the estimation process and enhances convergence of the learning algorithm. Our proposal is applied for personalized care of hemophiliac patients. Results show better performances than generative adversarial networks (baseline) and highlight the benefits of your contribution to predict the effect of treatments for patients

Un système tutoriel intelligent et adaptatif pour l'apprentissage de compétences en environnement virtuel de formation

This work takes place in the framework of the processing of environment for training using virtual reality. More particularly, it integrates the MASCARET project (Multi Agent System for Collaborative and Realistic Environment for Training). The objective is to develop a virtual environment for training dedicated to collaborative and procedural work.In this context, we defend the thesis that it is possible to integrate a generic and adaptive Intelligent Tutoring System (ITS) in a virtual environment, in order to provide pedagogical aid for the learner and pedagogical assistance for the trainer.This thesis begins with a study showing the interest of virtual reality and ITS for the acquisition of competences and identifies their difficulties. More particularly, it shows up the necessity of an abstract representation, independent of the exercise to realize, manipulable for pedagogical decision making and connected to the representation of a 3D universe.Our proposal is a multi-agent system allowing to analyse the action realized by the learner using an informed virtual environment.The system highlights a set of information, called pedagogical situation, considered relevant to make pedagogical decision.Then, our study is focused on a pedagogical agent. It suggests assistance to the trainer using the pedagogical situation. The abstraction used allows concrete assistance linked to the domain, to the exercise and to the virtual environment. The behavioural model of the pedagogical agent is based on a hierarchical classifiers system. Thanks to this model, the agent adapts itself to the trainer-learner pair, modifying its pedagogical behaviour by the way of an artificial learning mechanism based on a reinforcement provided by the trainer.This work is applied to the GASPAR project. The application simulates the plane activity on an aircraft-carrier.Ce travail se situe dans le cadre de la réalisation d'environnements de formation utilisant la réalité virtuelle. Plus précisément, il s'intègre dans le projet MASCARET (Multi Agent System for Collaborative and Realistic Environment for Training) dont l'objectif est de développer un environnement virtuel pour la formation au travail procédural et collaboratif.Dans ce contexte, nous soutenons la thèse qu'il est possible d'intégrer un système tutoriel intelligent (ITS) générique et adaptatif dans un environnement virtuel afin de fournir une aide pédagogique à l'apprenant et une assistance pédagogique au formateur.Cette thèse débute par une étude montrant l'intérêt mutuel de la réalité virtuelle et des ITS pour l'apprentissage de compétences, et identifie les difficultés de leur intégration. Plus précisément, elle souligne la nécessité d'une représentation abstraite, indépendante de l'exercice à réaliser, manipulable pour la prise de décision pédagogique et liée à la représentation d'un univers 3D.Notre proposition est un système multi-agents permettant d'analyser l'action réalisée par l'apprenant par le biais d'un environnement virtuel informé. Le système dégage un ensemble d'informations, appelé situation pédagogique, considéré pertinent pour la prise de décision pédagogique. Notre étude se focalise alors sur un agent pédagogique qui propose des assistances au formateur en utilisant la situation pédagogique. L'abstraction utilisée permet des assistances concrètes liées au domaine, à l'exercice et à l'environnement virtuel. Le modèle comportemental de l'agent pédagogique se base sur un système de classeurs hiérarchique. Grâce à ce modèle, l'agent s'adapte au couple apprenant-formateur en modifiant son comportement pédagogique par le biais d'un mécanisme d'apprentissage artificiel, basé sur un renforcement fourni par le formateur.Ces travaux sont appliqués dans le cadre du projet GASPAR (Gestion de l'Activité aviation et des Sinistres sur Porte-avions par la Réalité virtuelle). L'application simule l'activité aviation sur un porte-avions

Investigate naturalistic decision-making of a workgroup in dynamic situation. From the modelling to the design of a training virtual environment

International audienceThis thesis aims to rely on a work of activity analysis to develop a virtual training platform for firefighters (SécuRéVi). The use of this type of simulation is more and more common in the field of training, but often suffers from a lack of credibility in terms of learning content and method. To solve this problem, this project aims to model the collaborative work of firefighters during training sessions in order to provide assistance to the development of SécuRéVi. The activity analysis of these group works, relying on the EAST (Event Analysis of System Teamwork) methodology and self-confrontation interviews, is expected to highlight the particular "know-how" and to develop pedagogical scenarios essential in the design of such a training platform

Investigate Naturalistic Decision-Making of Football Players in Virtual Environment: Influence of Viewpoints in Recognition

International audienceIntroduction: The study of underlying processes of decision-making in dynamic situation, whether in work or in sport, is essential to the development of training tools. Virtual simulations are both key tools to study these processes and training. Method: Our work consisted in analysing the players' naturalistic decision-making in the virtual simulator CoPeFoot and the influence that changes of viewpoint can have on it. Behavioural data were recorded from six players in two different views (immersive and external), supplemented by verbal data collected during self-confrontation interviews. Results: A content analysis of the data revealed that in situations with strict time constraints, the players, to make decision, used twenty four schemata which facilitated the recognition of game situations. Discussion: These results points to the dynamic aspect of decision-making activity in the simulator and the consistency with the findings of studies in natural situations and the homogeneity for immersive and external views

PEGASE: A generic and adaptable intelligent system for virtual reality learning environments

International audienceThe context of this research is the creation of human learning environments using virtual reality. We propose the integration of a generic and adaptable intelligent tutoring system (Pegase) into a virtual environment. The aim of this environment is to instruct the learner, and to assist the instructor. The proposed system is created using a multi-agent system. This system emits a set of knowledge (actions carried out by the learner, knowledge about the field, etc.) which Pegase uses to make informed decisions. Our study focuses on the representation of knowledge about the environment, and on the adaptable pedagogical agent providing instructive assistance