Articles
Applied Soft Computing (15684946)156
Personalization of game difficulty is a critical task in leveraging artificial intelligence (AI) technologies to enhance player engagement in virtual worlds like metaverse. One of the key challenges in this area is developing methods for assessing a player's perception of game difficulty. This information can be used to dynamically adjust the game difficulty to match the player's skill level and preferences, which can improve the player's experience and engagement. The existing approaches have limitations such as relying on costly external devices, requiring time-consuming feedback or questionnaires, and being specific to certain game genres and narratives. In this paper, we propose a new method called ChatDDA for evaluating a player's perception of game difficulty by analyzing the content of their chat messages. Our method uses a pre-trained language model to extract semantic features from the chat messages, which are then used to train a feed-forward neural network to predict the player's level of hopefulness or despair about succeeding in the game. Three pre-trained language models—BERT, RoBERTa, and Twitter-roBERTa—are fine-tuned on a purpose-built dataset of player chat messages of the popular multiplayer online game PlayerUnknown's Battlegrounds (PUBG) tagged as expressing optimism or pessimism regarding game success. The results showed that our method can accurately predict a player's perception of game difficulty, with an accuracy of 0.953 on the test dataset of player chat messages. This suggests that our method has the potential to enhance player engagement and immersion within the game, ultimately leading to more satisfying and enjoyable metaverse experiences. © 2024 Elsevier B.V.
Multimedia Tools and Applications (13807501)83(10)pp. 31049-31079
The game industry is witnessing a significant trend of players toward massively multiplayer online games (MMO). Players are keen on forming teams and cooperating/competing in these games. Real-time measurement of players’ performance is one of the subjects of researchers’ attention to dynamically adjust the game difficulty and immerse players in the game. However, our extensive studies show that real-time measuring of teams’ skill levels has received much less attention. In this paper, a general real-time method called DeepSkill is proposed to measure the MMOs teams’ skills directly using players’ gameplay raw low-level data. The proposed method, which is based on the evidence-centered assessment design, was tested under six different configurations using popular machine learning techniques, including deep neural network (DNN), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), CatBoost, random forest (RF), and linear support vector regression (LinearSVR). According to the results, the proposed method provides accurate skill estimations and expertise level classifications. Specifically, Deepskill’s DNN-based evidence model provided the lowest mean absolute error of 0.09 in team skill estimation. Additionally, the proposed method achieved an accuracy of 0.973 in classifying the teams’ expertise level for the expert-novice classification task. Furthermore, a cost-effectiveness analysis was performed on the two top-performing evidence models. The LightGBM-based evidence model yielded the best results in both training and prediction phases in terms of low resource consumption alongside considerable accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
