Applying and Augmenting Deep Reinforcement Learning in Serious Games through Interaction

Dobrovsky, Aline; Borghoff, Uwe M.; Hofmann, Marko

doi:10.3311/ppee.10313

Cited by 17 publications

(4 citation statements)

References 29 publications

(33 reference statements)

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“…Adaptive gameplay attempts to address this issue by personalizing the plot, game complexity, and the amount of background information provided to the user [73]. Deep reinforcement learning has already demonstrated significant successes in automatically developing effective AI behavior and has the potential to improve serious games by utilizing subject experts' expertise to steer the learning process interactively [74,75].…”

Section: Model Of Knowledge Transfermentioning

confidence: 99%

Deep Reinforcement Learning-Based iTrain Serious Game for Caregivers Dealing with Post-Stroke Patients

et al. 2022

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This paper describes a serious game based on a knowledge transfer model using deep reinforcement learning, with an aim to improve the caretakers’ knowledge and abilities in post-stroke care. The iTrain game was designed to improve caregiver knowledge and abilities by providing non-traditional training to formal and informal caregivers who deal with stroke survivors. The methodologies utilized professional medical experiences and real-life evidence data gathered during the duration of the iTrain project to create the scenarios for the game’s deep reinforcement caregiver behavior improvement model, as well as the design of game mechanics, game images and game characters, and gameplay implementation. Furthermore, the results of the game’s direct impact on caregivers (n = 25) and stroke survivors (n = 21) in Lithuania using the Geriatric Depression Scale (GDS) and user experience questionnaire (UEQ) are presented. Both surveys had favorable outcomes, showing the effectiveness of the approach. The GDS scale (score 10) revealed a low number of 28% of individuals depressed, and the UEQ received a very favorable grade of +0.8.

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Section: Model Of Knowledge Transfermentioning

confidence: 99%

Deep Reinforcement Learning-Based iTrain Serious Game for Caregivers Dealing with Post-Stroke Patients

et al. 2022

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“…Mainly each one of them includes only one or two of the learning process components. like the studies [30], [32], [33], and [31] focused on the learner profile. finally, the studies in [29] and [34] concentrate on the learner profile and the learning objects.…”

Section: Gru-rnnmentioning

confidence: 99%

An Artificial Intelligence-Based Blockchain Framework for Improving the E-Learning System

Ahmed¹,

Helmy²,

Ouf³

2021

Journal of Southwest Jiaotong University

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The current era has witnessed a huge revolution in e-learning, as it was considered a lifeline under catastrophic conditions such as what happened during the Covid-19 pandemic. The importance of this case was spotlighted and took a significant shift. Many countries have used well-known e-learning platforms. UNESCO approved these e-learning platforms as a temporary solution. However, it did not recommend these platforms as a permanent solution because of the challenges found in the e-learning systems and affecting the learning process components. The study mentioned all the e-learning challenges that emerged and some techniques used to solve these challenges. It was found that some studies focused on e-learning challenges that are mainly related to the learner and the learning process using artificial intelligence techniques, while other studies have used blockchain technology as a solution to overcome the other challenges related to forged certificates and results manipulation. The paper proposed a framework that merges artificial intelligence and the blockchain to solve the challenges of e-learning process components. In addition, the study guaranteed a secure, private, and decentralized nature of the learner’s data in the university blockchain by implementing the blockchain phase. This framework includes the university blockchain development, mining new blocks, checking the blockchain validation, and adding transactions to improve the e-learning processes. Besides, artificial intelligence techniques will be employed to solve the challenges of the online exam and return effective learning outcomes for the learners.

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“…Some research work in the field of serious gaming frameworks exists. In particular, for the use of design patterns [12][13][14], approaches have been based on problem-based learning [15][16][17][18], deep learning [19,20], RETAIN model of FutureLab [21,22], and Evidence-Centered Design [23][24][25]. In this study, we use the Design, Play, and Experience (DPE) framework [26] to study the characteristics of SGs.…”

Section: Study Of Characteristics Of Serious Gamesmentioning

confidence: 99%

iPlus a User-Centered Methodology for Serious Games Design

et al. 2020

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Standard video games are applications whose development process often follows a traditional software methodology. Serious Games (SGs) are a tool with an immensely positive impact and great success. SGs enable learning and provide entertainment and self-empowerment, which motivates students. The development of an SG consists of complex processes requiring multi-disciplinary knowledge in multiple domains, including knowing the learning domain and adding the appropriate game mechanics to foster high intrinsic motivation and positive player experience that makes the players feel like they are having fun while learning. Otherwise, the game is viewed as boring and not as a fun and engaging activity. Nevertheless, despite their potential, the application of SGs in education has been limited in terms of pedagogy. Several authors assert that this lack is because SG standards and guidelines have not been developed. There is an imbalance between experts’ contributions to education and game design specialists for the SG development. Not all the SGs that have been developed have applied appropriate design methodologies that incorporate both the entertainment mechanics and the serious component. To ensure that an SG meets the user’s expectations, it must be designed using an appropriate method. This work aims to present iPlus, a methodology for designing SGs based on a participatory, flexible, and user-centered approach. Additionally, this paper analyses several case studies with the iPlus methodology.

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Applying and Augmenting Deep Reinforcement Learning in Serious Games through Interaction

Cited by 17 publications

References 29 publications

Deep Reinforcement Learning-Based iTrain Serious Game for Caregivers Dealing with Post-Stroke Patients

Deep Reinforcement Learning-Based iTrain Serious Game for Caregivers Dealing with Post-Stroke Patients

An Artificial Intelligence-Based Blockchain Framework for Improving the E-Learning System

iPlus a User-Centered Methodology for Serious Games Design

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