The Use of Agent-Based Models As Non-Player Characters in Serious Games

One of the shortcomings of many modern serious games and medical simulations lies in their inability to model even some modicum of unpredictability of real life situations. Interactions with a standardized patient may teach healthcare professional students how to diagnose a clinical condition, better manage a patient, or help them improve their bedside manners, but such simulated interactions will not prepare the learners to deal with unpredictability of clinical situations, interruption, and task switching. Distractions occur from colleagues, clinical decision support alerts, pagers, smartphones, or audible alarms. All these interruptions can potentially alter the course of patient care and the outcome of a patient's treatment. A simulated virtual patient (VP) may teach critical thinking skills, but once a student has successfully diagnosed a VP, the simulation stops providing educational value. In this paper we propose a generalizable method for integrating agent-based models into serious games and simulations. In the proposed paradigm, a human player (learner) takes on the role of a single agent in the model (e.g, a healthcare professional), while the output of the model controls the environment, the rules of agent interactions, and all the other agents that the human player interacts with (non-player characters). Moreover, we will present two use cases demonstrating that the use of agent-based models as behavior controllers for non-player characters introduces a degree of unpredictability in a virtual patient simulation and in a serious game designed to teach middle and high-school students about the spread of infectious diseases.