Browsing by Subject "Games"

Now showing 1 - 4 of 4
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    Maximum Density Divergence for Domain Adaptation
    (IEEE, 2021) Li, Jingjing; Chen, Erpeng; Ding, Zhengming; Zhu, Lei; Lu, Ke; Shen, Heng Tao; Computer Information and Graphics Technology, Purdue School of Engineering and Technology
    Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptation is to mitigate the distribution divergence between the two domains. The state-of-the-art methods practice this very idea by either conducting adversarial training or minimizing a metric which defines the distribution gaps. In this paper, we propose a new domain adaptation method named adversarial tight match (ATM) which enjoys the benefits of both adversarial training and metric learning. Specifically, at first, we propose a novel distance loss, named maximum density divergence (MDD), to quantify the distribution divergence. MDD minimizes the inter-domain divergence ("match" in ATM) and maximizes the intra-class density ("tight" in ATM). Then, to address the equilibrium challenge issue in adversarial domain adaptation, we consider leveraging the proposed MDD into adversarial domain adaptation framework. At last, we tailor the proposed MDD as a practical learning loss and report our ATM. Both empirical evaluation and theoretical analysis are reported to verify the effectiveness of the proposed method. The experimental results on four benchmarks, both classical and large-scale, show that our method is able to achieve new state-of-the-art performance on most evaluations.
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    Remote Assessment of ADHD Symptoms Based on Mobile Game Performance in Children with ADHD: A Proof of Concept
    (IEEE, 2023-07) Song, Jeong-Heon; Kim, Byeongil; Kim, Seon-Chil; Toom, Niharika; Kaur, Charanjit; Rodriguez, Gabriela Marie; Hord, Melissa Kay; Jung, Hee-Tae; Psychiatry, School of Medicine
    The use of game-based digital medicine is gaining increasing interest in helping children with ADHD to improve their attention outside the clinical setting. In this process, it is important to continue monitoring children’s responses to the use of digital medicine. In this work, we introduce novel digital markers and an analytic pipeline to estimate ADHD-related symptomatic levels during the self-administration of attention games. The digital markers, capturing the children’s characteristics of attention and inattention spans, were extracted and translated into clinically-accepted measures of ADHD symptoms, specifically the ADHD-Rating Scale (ADHD-RS) and Child Behavior Checklist (CBCL). To validate the feasibility of our approach, we collected game-specific performance data from 15 children with ADHD, which was used to train machine learning-based regression models to estimate their corresponding ADHD-RS and CBCL scores. Our experiment results showed mean absolute errors of 5.14 and 4.05 points between the actual and estimated ADHD-RS and CBCL scores respectively. This study enables new clinical and research opportunities for accurate longitudinal assessment of symptomatic levels of ADHD via an interactive means of playing mobile games.
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    The Effect of Behavioral Probability Weighting in a Simultaneous Multi-Target Attacker-Defender Game
    (IEE, 2021) Abdallah, Mustafa; Cason, Timothy; Bagchi, Saurabh; Sundaram, Shreyas; Electrical and Computer Engineering, Purdue School of Engineering and Technology
    We consider a security game in a setting consisting of two players (an attacker and a defender), each with a given budget to allocate towards attack and defense, respectively, of a set of nodes. Each node has a certain value to the attacker and the defender, along with a probability of being successfully compromised, which is a function of the investments in that node by both players. For such games, we characterize the optimal investment strategies by the players at the (unique) Nash Equilibrium. We then investigate the impacts of behavioral probability weighting on the investment strategies; such probability weighting, where humans overweight low probabilities and underweight high probabilities, has been identified by behavioral economists to be a common feature of human decision-making. We show via numerical experiments that behavioral decision-making by the defender causes the Nash Equilibrium investments in each node to change (where the defender overinvests in the high-value nodes and underinvests in the low-value nodes).
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    Theory and Research into Practice: Using Self-Determination Theory to Analyze Gamification and Motivational Affordances in Serious Games for Health Education
    (Office of the Vice Chancellor for Research, 2016-04-08) Hill, Jacqueline; Jia, Yuan; Defazio, Joseph
    In serious games for health education, regulation styles in gamification help the player achieve goals through behavioral motivations that may not be apparent in an educational activity. These regulation styles are referred to as motivational affordances and might be widely employed in gamification or game-like systems that motivate users to engage in play a “gameful-type” experience. Gamification is defined as the application of game mechanics (point scoring, competition, rules, etc.) and game design techniques in order to engage and motivate players to achieve goals. Zhang defined affordance as “the actionable properties between an object and an actor” which determine how they can support one’s motivational needs. Are there common gamification and motivational affordances in serious games that prove to be effective in game play on the topic of diabetes? To answer this question, the authors explore the effects of extrinsic and intrinsic motivation during game play by analyzing six serious games in health education on the topic of diabetes. The games selected for this study are: Carb Counting with Lenny, Dex: Your Virtual Pet, Pancreas!, I Got This, Packy & Marlon, and Captain Novolin. As a work-in-progress, the authors provide evidence using the Self-Determination Theory (SDT) and the constructs of motivational affordances. The characteristics in SDT were identified as competence, relatedness, and autonomy. In addition, the author’s research explores motivational affordances found in these health education games namely: psychological outcomes: motivation – keeping the player engaged, attitude – the effect of solving a problem or challenge, and enjoyment – a behavioral outcome produced by competition, play, and achievement. Behavioral outcomes: achievement – attaining success, learning evidence – gaining new knowledge (learning outcome), participation – player immersion in the game objective or story. The authors present their findings and identify, compare, and contrast gamification and the motivational affordances found in each game. Mentor: Joseph Defazio Ph.D., Department of Human-Centered Computing, IU School of Informatics and Computing, IUPUI; Indianapolis, IN