An Adaptive System for Emerging Serious Games Using a Swarm Intelligence Algorithm

Abstract

An emerging serious game (SG) is a game designed and developed from an objective other than pure fun, so that the player can learn while playing, but where the game unfolds spontaneously, autonomously, and without explicit laws, adapting to the player. On the other hand, an emerging serious game engine (ESGE) must make explicit the possibility of emergence in an SG, from the coordinated handling of the game plot, adapted to the specific educational context where it is being used. In previous articles, an ESGE has been proposed, which allows the initial emergence of an SG. This article defines a new component of the ESGE, called the plot adaptive system (PAS), which allows the updating of an SG according to the theme that is being given in a smart classroom (SaCI). This component is based on an ant colony optimization algorithm that changes the plots/frames in the game to follow the desired theme in the SaCI. Thus, this new component manages a set of game plots that may be of interest in an educational context to dynamically modify an SG initially conceived for a subject taught in the SaCI, in order to adequate it to the current pedagogical context. The novelty of the approach is that it adapts the SG to the educational context where it takes place. Additionally, this article analyzes the behavior of the PAS in a case study, and presents three experiments with very encouraging results.