ABSTRACT In this paper, we present a generic interaction framework that controls intelligent objects' actions in different virtual world (VW) platforms. These actions are based on the state of external platform‐independent artificial intelligence‐based systems such as multi‐agent and rule‐based systems. We have evaluated the proposed framework by means of two intelligent objects, a door and a noticeboard, incorporating them in Second Life and OpenWonderland VWs. These objects allow to work along three advanced aspects of a serious virtual environment: it is a dynamic space where participants are synchronously informed about activities evolution inside the virtual space, it can integrate processes and information coming from the real world, and it needs norms to organize participants actions, to define actions consequences, and to prevent undesired participants behaviors. Copyright © 2011 John Wiley & Sons, Ltd.

Our research combines electronic institutions and 3D virtual worlds for the construction of virtual institutions which are virtual worlds with normative regulation of interactions. That is, a virtual world where participants actions have to comply with predefined institutional rules. In this context, the actions a participant may perform depend on the institutional rules and the current execution state. We propose to include iObjects, intelligent objects, as entities having both visualization properties and decision mechanisms in the virtual institution. They are a new key element to improve users participation in virtual institutions. We situate them in a middleware infrastructure in order to be independent of 3D virtual world platform and to provide a general solution in which participants could be connected from different immersive environment platforms.

Engineering individual components of a multi-agent system and their interactions is a complex and error-prone task in urgent need of methods and tools. Prototyping is a valuable technique to help software engineers explore the design space while gaining insight and a "feel" for the dynamics of the system; prototyping also allows engineers to learn more about the relationships among design features and the desired computational behaviour. In this paper we describe an approach to building prototypes of large multi-agent systems with which we can experiment and analyse results. We have implemented an environment embodying our approach. This environment is supported by a distributed platform that helps us achieve controlled simulations.