A Learning-Embedded Attributed Petri Net to Optimize Student Learning in a Serious Game

Liang, Joleen; Tang, Ying; Hare, Ryan; Wu, Ben; Wang, Fei-Yue

doi:10.1109/tcss.2021.3132355

Cited by 14 publications

(9 citation statements)

References 31 publications

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“…In addition, many technology enterprises, including Microsoft, NVIDIA, and Tencent, have participated in the concept of metaverse. As a key technique of metaverse, virtual reality has already been utilized in the serious game to improve educational effectiveness [30], [31], [32], [33], [34]. Furthermore, the work in [35] based its view on metaverse-powered online distance education.…”

Section: A Metaversementioning

confidence: 99%

MARL Sim2real Transfer: Merging Physical Reality With Digital Virtuality in Metaverse

Shi

Liu

Zhang

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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Metaverse is an artificial virtual world mapped from and interacting with the real world. In metaverse, digital entities coexist with their physical counterparts. Powered by deep learning, metaverse is inevitably becoming more intelligent in the interactions between reality and virtuality. However, it is confronted with a nontrivial problem known as sim2real transfer when deep learning techniques try to bridge the reality gap between the physical world and simulations. In this article, we use multiagent deep reinforcement learning (MARL) to implement collective intelligence for digital entities as well as their physical counterparts. To model the immersive environments in metaverse, we define a nonstationary variant of Markov games and propose a recurrent MARL solution to it. Based on the solution, MARL sim2real transfer that bridges real and virtual multiple unmanned aerial vehicle (multi-UAV) systems is successfully conducted by employing recurrent multiagent deep deterministic policy gradient (R-MADDPG) with the domain randomization technique. Additionally, we use perception-control modularization to improve the generalization performance of MARL policies and make training more efficient.

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Section: A Metaversementioning

confidence: 99%

MARL Sim2real Transfer: Merging Physical Reality With Digital Virtuality in Metaverse

Shi

Liu

Zhang

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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“…In fact, Metaverse learning resonates much with our earlier development of Parallel Intelligent Education as shown in Fig. 1 [3]. Furthermore, the Metaverse creates a great starting point from which to construct a new generation of cyber-physical-social systems to interface with learners and expand education through human-computer interactions.…”

Section: Introductionmentioning

confidence: 75%

“…FOR METAVERSE LEARNING The ACP approach has been widely used in several application domains since its inception by Wang [18], such as discrete-time nonlinear control [19], healthcare [20], education [3], and motion recognition [21]. Extended from our prior work, Fig.…”

Section: Acp-based Parallel Controlmentioning

confidence: 99%

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Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education

Hare

Tang

2023

IEEE Trans. Syst. Man Cybern, Syst.

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Metaverse has gained increasing interest in education, with much of literature focusing on its great potential to enhance both individual and social aspects of learning. However, little work has been done to address the systems and technologies behind providing meaningful Metaverse learning. This article proposes a technical framework to address this research gap, where a hierarchical multiagent reinforcement learning approach with experience sharing is developed to augment the intelligence of nonplayer characters in Metaverse learning for personalization. The utility and benefits of the proposed framework and methodologies are demonstrated in Gridlock, a Metaverse learning game, as well as through extensive simulations.

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“…Some authors have reviewed intelligent adaptation methods used in serious games for education, finding that the most used were rule-based, Bayesian, fuzzy, and reinforcement learning [ 42 ]. Another related technique to personalization in serious games was incorporating dynamics of learner behaviors as learning attributes in a Petri net model for knowledge reasoning and learning [ 43 ]. Tang et al [ 44 ] describe an intelligent component based on the k-nearest-neighbor (kNN) classification method to provide personalized feedback and guide the student through the learning process.…”

Section: Related Workmentioning

confidence: 99%

Personalized Gamification for Learning: A Reactive Chatbot Architecture Proposal

González-González

Muñoz-Cruz

Toledo-Delgado

et al. 2023

Sensors

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A key factor for successfully implementing gamified learning platforms is making students interact with the system from multiple digital platforms. Learning platforms that try to accomplish all their objectives by concentrating all the interactions from users with them are less effective than initially believed. Conversational bots are ideal solutions for cross-platform user interaction. In this paper, an open student–player model is presented. The model includes the use of machine learning techniques for online adaptation. Then, an architecture for the solution is described, including the open model. Finally, the chatbot design is addressed. The chatbot architecture ensures that its reactive nature fits into our defined architecture. The approach’s implementation and validation aim to create a tool to encourage kids to practice multiplication tables playfully.

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A Learning-Embedded Attributed Petri Net to Optimize Student Learning in a Serious Game

Cited by 14 publications

References 31 publications

MARL Sim2real Transfer: Merging Physical Reality With Digital Virtuality in Metaverse

MARL Sim2real Transfer: Merging Physical Reality With Digital Virtuality in Metaverse

Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education

Personalized Gamification for Learning: A Reactive Chatbot Architecture Proposal

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