The theory of affordances

This paper reviews the current state of the art for Virtual Reality (VR) and Virtual Environment (VE) applications in the field of the built environment. The review begins with a brief overview of technological components involved in enabling VR technology. A classification framework is developed to classify 150 journal papers in order to reveal the scholarly coverage of VR and VE from 2005 to 2011, inclusive. The classification framework summarizes achievements, established knowledge, research issues and challenges in the area. The framework is based on four layers of VR: concept and theory, implementation, evaluation and industrial adoption. These layers encompass architecture and design, urban planning and landscape, engineering, construction, facility management, lifecycle integration, training and education. This paper also discusses various representative VR research work in line with the classification framework. Finally the paper predicts future research trends in this area.

/pdf/virtual-reality-for-the-built-environment-a-critical-review-2rmd6dzoq2.pdf

Virtual reality for the built environment: a critical review of recent advances

OMAS-a flexible multi-agent environment for CSCWD

The 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.

https://www.enib.fr/~buche/article/IJVR_10.pdf

PEGASE: A Generic and Adaptable Intelligent System for Virtual Reality Learning Environments

Modelling Autonomous Virtual Agent Behaviours in a Virtual Environment for Risk.

Our research deals with the design of a training system to support decision-making in the preparation and the management of maintenance interventions in high-risk industries namely SEVESO sites. The proposed system incorporates virtual reality and artificial intelligence to simulate virtual autonomous characters and their cognitive processes in dangerous working situations. It generates behaviour-based errors to support learning and risk prevention. It uses new mechanisms taking into account human factors with respect to cognitive modelling of human behaviour regarding risky situations. In the simulated environment the trainee can visualize the risks incurred during his work with the virtual agents. The emergent risks depend on the cognitive characteristics of the virtual operators and on the expertise of the trainee. We propose a multi-agent system to support the control of virtual operators represented by virtual cognitive agents. The difference with a classic MAS is that our cognitive agents are enriched with a planner for selecting actions according to goals, the environment and to the personal characteristics of the agents (time pressure, caution, tiredness, hunger).

Modelling autonomous virtual agents and behaviours in a virtual environment for risk.

We introduce a cognitive architecture combining human factors, human cognitive modeling, decision, perception and action selection based on hierarchical task networks. We use a cognitive model that comes from phycology science to define the control mode of a virtual agent. This control mode influences the agent decisions as well as its perception, its knowledge, or its goals. It depends on its physical and cognitive states and on its personality. We determine which factors influence the agent control mode and use them to generate various behaviors according to the situational constraints. We propose a new approach for modeling the environment and new algorithms to enable our virtual characters to evolve in the environment dynamically with a certain level of credibility.

Cognitive Modeling of Virtual Autonomous Intelligent Agents Integrating Human Factors

The new possibilities offered by virtual reality in terms of behavioural modelling and the emergence of knowledge engineering tools for risks analysis give the opportunity to envisage tools to improve training and decision making for preventing risks. In the V3S (Virtual Reality for Safe Seveso Substractors) project, we design a tool allowing storyboarding hazardous working situations on Seveso sites for risk prevention and decision making. The tool interprets a high level task and a related risk model. It is meant for a manager to help him/her to make decisions. The manager plays the scenario of an intervention and manages a tean of virtual operators (associated with autonomous agents). Depending on his/her decisions, the incurred risks are displayed in the virtual environment. In this paper, we present our project and the multi-agent architecture on which it relies.

Multi-agents approach for modelling safety interventions on a SEVESO site through virtual reality

Many day-to-day applications involve autonomous agents. When designing autonomous agents, the problem of selecting actions must be considered, as it governs decision making at all times. In this paper we describe how we designed intelligent virtual cognitive agents representing operators performing tasks in a high-risk plant. They must respond to expected as well as unexpected events. The reasoning system controlling the agent behavior is designed to exhibit human behavior. Agents must be able to plan their actions according to their perception, their beliefs and their goals. We developed a planning system that interleaves plan construction and plan execution. The new planner, called AATP, produces a plan according to agent goal.

An Action Selection Architecture for Autonomous Virtual Agents

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