Abstract

Abstract

Abstract

Abstract
Regardless of the goal of a game, it should be a pleasant and fun experience for its players. For some games to be enjoyable, the level of difficulty must be carefully calibrated, otherwise, players will feel bored or frustrated. Multiplayer scenarios in particular, where one player's satisfaction might not translate to the enjoyment of other players and poses extra challenges in balancing the difficulty. The performance of one player is relative to the opponent, versus single-player scenarios where we can fully control the environment. We propose an AI automation framework for difficulty balancing in two-player games, where balancing is seen as a Reinforcement Learning task. A Game Master (GM) agent learns how to use handicap game mechanics, signaled by a reward function that evaluates a weighted combination of aesthetic criteria that encourages dramatization and allows a player in the lead to go back and a player in the rear to catch up, creating the desired rubber banding effect that balances out skill gaps. The quality of the games with the trained GM embedded is examined by measuring the same aesthetic criteria on the resulting games, and by analyzing the resulting changes in the game.

Abstract
In the last decades most efforts to catalog and characterize the built environment for multi-hazard risk assessment have focused on the exploration of census data, cadastral datasets, and local surveys. The first approach is only updated every 10 years and does not provide building locations, the second type of data is only available for restricted urban centers, and the third approach requires surveyors with an engineering background, which is cost-prohibitive for large-scale risk studies. It is thus clear that methods to characterize the built environment for large-scale risk analysis at the asset level are currently missing, which hampers the assessment of the impact of natural hazards for the purposes of risk management. Some recent efforts have demonstrated how deep learning algorithms can be trained to recognize specific architectural and structural features of buildings, which is needed for earthquake risk analysis. In this paper we describe how convolutional neural networks can be combined with data from OpenStreetMap and Google Street View to help develop exposure models for multi-hazard risk analysis. This project produced an original comprehensively annotated (15 characteristics) dataset of approximately 5000 images of buildings from the parish of Alvalade (Lisbon, Portugal). The dataset was used to train and test different deep learning networks for building exposure models. The best results were obtained with ResNet50V2, InceptionV3 and DenseNet201, all with accuracies above 82%. These results will support future developments for assessing exposure models for seismic risk analysis. The novelty of our work consists in the number of characteristics of the images in the dataset, the number of deep learning models trained and the number of classes that can be used for building exposure models.

Abstract
Information and communication technologies, such as serious games, have contributed to addressing the gaps in cognitive rehabilitation for individuals with acquired brain injury (ABI), particularly in the context of the COVID-19 pandemic. Although there are effective software programs and games available for cognitive rehabilitation, they have certain limitations. Most current programs have difficulties to adapt to individual performance, a critical factor in promoting neuroplasticity. Additionally, these programs typically only offer single-player modes. However, patients experience difficulties in social interactions leading to social isolation. To overcome these limitations, we propose a novel platform called CogniChallenge. It introduces multiplayer serious games designed for cognitive and psychosocial rehabilitation, offering competitive and cooperative game modes. This platform facilitates engagement with other patients, family members, caregivers, and virtual agents that simulate human interaction. CogniChallenge consists of three games based on activities of daily life and incorporates a multi-agent game balance system. Future research endeavors will focus on evaluating the usability and gameplay experience of CogniChallenge among healthcare professionals and individuals with ABI. By proposing this innovative platform, we intend to contribute to expanding the application of serious games and their potential to solve problems and limitations in the specific field of cognitive rehabilitation. © 2023, Serious Games Society. All rights reserved.