AViLab—Gamified Virtual Educational Tool for Introduction to Agent Theory Fundamentals

Abstract

The development and increased popularity of interactive computer games, metaverses, and virtual worlds in general, has over the years attracted the attention of various researchers. Therefore, it is not surprising that the educational potential of these virtual environments (e.g., virtual laboratories) is of particular interest to a wider scientific community, with numerous successful examples coming from different fields, starting from social sciences, to STEM disciplines. However, when it comes to agent theory, which is a highly important part of the general AI (Artificial Intelligence) research focus, there is a noticeable absence of such educational tools. To be more precise, there is a certain lack of virtual educational systems dedicated primarily to agents. That was the motivation for the development of the AViLab (Agents Virtual Laboratory) gamified system, as a demonstration tool for educational purposes in the related subject of agent theory. The developed system is thoroughly described in this paper. The current version of the AViLab consists of several agents (developed according to the agenda elaborated in the manuscript), aiming to demonstrate certain insights into fundamental agent structures. Although the task imposed to our agents essentially represents a sort of “picking” or “collecting” task, the scenario in the system is rather gamified, in order to be more immersive for potential users, spectators, or possible test subjects. This kind of task was chosen because of its wide applicability in both, gaming scenarios and real-world everyday scenarios. In order to demonstrate how AViLab can be utilized, we conducted an exemplar experiment, described in the paper. Alongside its educational purpose, the AViLab system also has the potential to be used for research purposes in the related subjects of agent theory, AI, and game AI, especially regarding future system extensions (including the introduction of new scenarios, more advanced agents, etc.).