Review of Intrinsic Motivation in Simulation-based Game Testing

This paper presents a review of intrinsic motivation in player modeling, with a focus on simulation-based game testing. Modern AI agents can learn to win many games; from a game testing perspective, a remaining research problem is how to model the aspects of human player behavior not explained by purely rational and goal-driven decision making. A major piece of this puzzle is constituted by intrinsic motivations, i.e., psychological needs that drive behavior without extrinsic reinforcement such as game score. We first review the common intrinsic motivations discussed in player psychology research and artificial intelligence, and then proceed to systematically review how the various motivations have been implemented in simulated player agents. Our work reveals that although motivations such as competence and curiosity have been studied in AI, work on utilizing them in simulation-based game testing is sparse, and other motivations such as social relatedness, immersion, and domination appear particularly underexplored

Prediction of hospital length of stay to achieve flexible healthcare in the field of Internet of Vehicles

International audienceThe patient transfer from hospitals to followup care and rehabilitation facilities is an important aspect for maintaining the continuity of medical care. In order to achieve flexible healthcare within the field of Internet of Vehicles (IoVs) in terms of secure patient transfer and ambulance transport, the whole organization of patients' discharge and transfer should be anticipated, based mostly on a length of stay (LOS) given at the time of inpatient admission. Therefore, the prediction of LOS has serious impact on influx coordination, bed management, ambulance scheduling, and furthermore, on the financial balance of hospitals. Based on studying medical data, the prediction with good accuracy can help hospital managers get an efficient and robust resource management. The challenge is then how to extract valuable information from medical data, which contains considerable hesitation and uncertainty elements. In this article, a hesitant fuzzy-rough nearest-neighbor algorithm has been proposed and experimented with real medical data. Hesitation interpretation has been reflected in the process of determining class labels in our algorithm via hesitant fuzzy relation determination and hesitant fuzzy-rough similarity measure. The experimental analysis has shown that the proposed algorithm has better performance and extensibility