Cédric Buche

Transfer of learning in virtual environments: a new challenge?

The aim of all education is to apply what we learn in different contexts and to recognise and extend this learning to new situations. Virtual learning environments can be used to build skills. Recent research in cognitive psychology and education has shown that acquisitions are linked to the initial context. This provides a challenge for virtual reality in education or training. A brief overview of transfer issues highlights five main ideas: (1) the type of transfer enables the virtual environment (VE) to be classified according to what is learned; (2) the transfer process can create conditions within the VE to facilitate transfer of learning; (3) specific features of VR must match and comply with transfer of learning; (4) transfer can be used to assess a VE’s effectiveness; and (5) future research on transfer of learning must examine the singular context of learning. This paper discusses how new perspectives in cognitive psychology influence and promote transfer of learning through the use of VEs.

MASCARET: A Pedagogical Multi-Agent System for Virtual Environments for Training

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Mascaret: Pedagogical multi-agents system for virtual environment for training. Cédric Buche, Ronan Querrec, Pierre De Loor, Pierre Chevaillier

An expert system manipulating knowledge to help human learners into virtual environment

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

Fuzzy cognitive maps for the simulation of individual adaptive behaviors

This paper focuses on the simulation of behavior for autonomous entities in virtual environments. The behavior of these entities must determine their responses not only to external stimuli, but also with regard to internal states. We propose to describe such behavior using fuzzy cognitive maps (FCMs), whereby these internal states might be explicitly represented. This paper presents the use of FCMs as a tool to specify and control the behavior of individual agents. First, we describe how FCMs can be used to model behavior. We then present a learning algorithm allowing the adaptation of FCMs through observation. Copyright

Anticipatory behavior in virtual universe, application to a virtual juggler

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

MASCARET: creating virtual learning environments from system modelling

The design process for a Virtual Learning Environment (VLE) such as that put forward in the SIFORAS project (SImulation FOR training and ASsistance) means that system specifications can be differentiated from pedagogical specifications. System specifications can also be obtained directly from the specialists’ expertise; that is to say directly from Product Lifecycle Management (PLM) tools. To do this, the system model needs to be considered as a piece of VLE data. In this paper we present Mascaret, a meta-model which can be used to represent such system models. In order to ensure that the meta-model is capable of describing, representing and simulating such systems, MASCARET is based SysML1, a standard defined by Omg.

Chameleon: online learning for believable behaviors based on humans imitation in computer games

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

Agent Metamodel for Virtual Reality Applications

The various existing agent models do not cover all the possible uses we consider for virtual reality applications. In this paper, we present an agent metamodel (Behave) based on an environment metamodel (Veha). This metamodel allows defining agents and organizing teams of agents in a virtual environment. The use of this metamodel is illustrated by the Gaspar application which simulates activities on an aircraft carrier.

Simulation theory and anticipation for interactive virtual character in an uncertain world

This paper deals with simulations of real‐time interactive character behavior. The underlying idea is to take into account principles from cognitive science, in particular, the human ability to anticipate and simulate the world behavior. For that purpose, we propose a conceptual framework where the entity possesses an autonomous world of simulation within simulation, in which it can simulate itself (with its own model of behavior) and the environment (with an abstract representation, which can be learnt, of the other entities behaviors). This principle is illustrated by the development of an artificial juggler, which predicts the motion of balls in the air and uses its predictions to coordinate its own behavior while juggling. Thanks to this model it is possible to add a human user to launch balls that the virtual juggler can catch whilst juggling. Copyright

Les systèmes de classeurs : Une présentation générale

This article concerns the presentation of classifiers systems built to learn interactions. Different kinds of classifiers systems are presented, using a chronological approach introducing new challenges or concepts approached by every model. We describe used concepts: genetic algorithms and reinforcement learning. The article presents basics systems such as ZCS or XCS, systems for anticipation, as well as hierarchicals or heterogenous classifiers.