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Experience-driven PCG via reinforcement learning: A Super Mario Bros study

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arXiv 2021年

作者： Shu, Tianye Liu, Jialin Yannakakis, Georgios N. Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Institute of Digital Games University of Malta Msida Malta

We introduce a procedural content generation (PCG) framework at the intersections of experience-driven PCG and PCG via reinforcement learning, named ED(PCG)RL, EDRL in short. EDRL is able to teach RL designers to generate endless playable levels in an online manner while respecting particular experiences for the player as designed in the form of reward functions. The framework is tested initially in the Super Mario Bros game. In particular, the RL designers of Super Mario Bros generate and concatenate level segments while considering the diversity among the segments. The correctness of the generation is ensured by a neural net-assisted evolutionary level repairer and the playability of the whole level is determined through AI-based testing. Our agents in this EDRL implementation learn to maximise a quantification of Koster's principle of fun by moderating the degree of diversity across level segments. Moreover, we test their ability to design fun levels that are diverse over time and playable. Our proposed framework is capable of generating endless, playable Super Mario Bros levels with varying degrees of fun, deviation from earlier segments, and playability. EDRL can be generalised to any game that is built as a segment-based sequential process and features a built-in compressed representation of its game content. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

SSF-DAN: Separated Semantic Feature Based Domain Adaptation Network for Semantic Segmentation

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SSF-DAN: Separated Semantic Feature Based Domain Adaptation ...

International Conference on computer Vision (ICCV)

作者： Liang Du Jingang Tan Hongye Yang Jianfeng Feng Xiangyang Xue Qibao Zheng Xiaoqing Ye Xiaolin Zhang Bionic Vision System Laboratory State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China School of Computer Science Fudan University China Baidu Inc. ShanghaiTech University Shanghai China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Despite the great success achieved by supervised fully convolutional models in semantic segmentation, training the models requires a large amount of labor-intensive work to generate pixel-level annotations. Recent works exploit synthetic data to train the model for semantic segmentation, but the domain adaptation between real and synthetic images remains a challenging problem. In this work, we propose a Separated Semantic Feature based domain adaptation network, named SSF-DAN, for semantic segmentation. First, a Semantic-wise Separable Discriminator (SS-D) is designed to independently adapt semantic features across the target and source domains, which addresses the inconsistent adaptation issue in the class-wise adversarial learning. In SS-D, a progressive confidence strategy is included to achieve a more reliable separation. Then, an efficient Class-wise Adversarial loss Reweighting module (CA-R) is introduced to balance the class-wise adversarial learning process, which leads the generator to focus more on poorly adapted classes. The presented framework demonstrates robust performance, superior to state-of-the-art methods on benchmark datasets.

关键词： Semantics Image segmentation Generators Task analysis Adaptation models Feature extraction Data models

A Novel Dual-Stage Algorithm for Capacitated Arc Routing Problems with Time-Dependent Service Costs

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SSRN

SSRN 2024年

作者： Li, Qingya Liu, Shengcai Zou, Juan Tang, Ke Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Xiangtan411105 China The Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan411105 China

This paper focuses on solving the capacitated arc routing problem with time-dependent service costs (CARPTDSC), which is motivated by winter gritting applications. In the current literature, exact algorithms designed for CARPTDSC can only handle small-scale instances, while heuristic algorithms fail to obtain high-quality solutions. To overcome these limitations, we propose a novel dual-stage algorithm, called MAENS-GN, that consists of a routing stage and a vehicle departure time optimization stage. The former obtains the routing plan, while the the latter determines the vehicle departure time. Importantly, existing literature often ignores the characteristic information contained in the relationship between the route cost and the vehicle departure time. The most signiﬁcant innovation in this paper lies in the exploitation of this characteristic information during the vehicle departure time optimization stage. Speciﬁcally, we conduct a detailed analysis of this relationship under various scenarios and employ tailored methods to obtain the (approximately) optimal vehicle departure time. Furthermore, we propose an improved initialization strategy that considers time-dependent characteristics to achieve better solution quality. In addition to the modiﬁed benchmark test sets, we also experiment on a real-world test set. Experimental results demonstrate that MAENS-GN can obtain high-quality solutions on both small-scale and larger-scale instances of CARPTDSC. © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

Sampling methods for efficient training of graph convolutional networks: A survey

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arXiv 2021年

作者： Liu, Xin Yan, Mingyu Deng, Lei Li, Guoqi Ye, Xiaochun Fan, Dongrui State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100086 China Department of Precision Instrument Center for Brain Inspired Computing Research Tsinghua University Beijing100084 China School of Computer Science and Technology University of Chinese Academy of Sciences

—Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations. Although GCN performs well compared with other methods, it still faces challenges. Training a GCN model for large-scale graphs in a conventional way requires high computation and storage costs. Therefore, motivated by an urgent need in terms of efficiency and scalability in training GCN, sampling methods have been proposed and achieved a significant effect. In this paper, we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of GCN. To highlight the characteristics and differences of sampling methods, we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all categories. Finally, we discuss some challenges and future research directions of the sampling methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

Enhanced Assessment of Auditory Perception Function in Disorders of Consciousness Using a Minimalist Wearable brain-computer interface

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Enhanced Assessment of Auditory Perception Function in Disor...

computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Chenxin Xue Jun Xiao Huang Wu Li Hu Jiahui Pan Yuanqing Li School of Automation Science and Engineering South China University of Technology Guangzhou China School of Electric Power Engineering South China University of Technology Guangzhou China School of Software South China Normal University Guangzhou China Research Center for Brain-Computer Interface Pazhou Laboratory Guangzhou China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

The Coma Recovery Scale-Revised (CRS-R) is a clinical behavioral method for assessing the levels of consciousness and perceptual abilities in patients with Disorders of Consciousness (DOC). However, due to severe impairment of motor functions in DOC patients and subjective factors of clinical evaluators, the misdiagnosis rate of the CRS-R is very high. brain-computer interfaces (BCIs) can directly detect patients’ brain activity in response to external stimuli, independent of patients’ behavioral representation, thereby achieving a more objective assessment. In this study, we introduced a minimalist wearable BCI system grounded in the auditory oddball paradigm to enhance assessment of auditory perception function in DOC patients. In the experiments, sinusoidal tones and the complex sound of clapping were selected as standard stimuli occurring frequently and occasional deviant stimuli, respectively. The specific event-related potentials evoked by the auditory stimuli facilitated the assessment of auditory perceptual function. The experimental results indicate that the DOC patients with auditory perception functions can also be detected by the BCI system. As a result, the BCI could serve as a valuable adjunct tool in clinical practice of the CRS-R.

关键词： brain External stimuli Medical services Big Data Motors brain-computer interfaces Electroencephalography

Measuring Diversity of Game Scenarios

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arXiv 2024年

作者： Li, Yuchen Wang, Ziqi Zhang, Qingquan Yuan, Bo Liu, Jialin Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Tandon School of Engineering New York University United States School of Data Science Lingnan University Hong Kong

This survey comprehensively reviews the metrics for measuring the diversity of game scenarios, spotlighting the innovative use of procedural content generation and other fields as cornerstones for enriching player experiences through diverse game scenarios. By traversing a wide array of disciplines, from affective modeling and multi-agent systems to psychological studies, our research underscores the importance of diverse game scenarios in gameplay and education. Through a taxonomy of diversity metrics and evaluation methods, we aim to bridge the current gaps in literature and practice, offering insights into effective strategies for measuring and integrating diversity in game scenarios. Our analysis highlights the necessity for a unified taxonomy to aid developers and researchers in crafting more engaging and varied game worlds. This survey not only charts a path for future research in diverse game scenarios but also serves as a handbook for industry practitioners seeking to leverage diversity as a key component of game design and development. Copyright © 2024, The Authors. All rights reserved.

关键词： Taxonomies

Fairness-aware Multiobjective Evolutionary Learning

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arXiv 2024年

作者： Zhang, Qingquan Liu, Jialin Yao, Xin The Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen518055 China The Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The School of Data Science Lingnan University Hong Kong

Multiobjective evolutionary learning (MOEL) has demonstrated its advantages of training fairer machine learning models considering a predefined set of conflicting objectives, including accuracy and different fairness measures. Recent works propose to construct a representative subset of fairness measures as optimisation objectives of MOEL throughout model training. However, the determination of a representative measure set relies on dataset, prior knowledge and requires substantial computational costs. What's more, those representative measures may differ across different model training processes. Instead of using a static predefined set determined before model training, this paper proposes to dynamically and adaptively determine a representative measure set online during model training. The dynamically determined representative set is then used as optimising objectives of the MOEL framework and can vary with time. Extensive experimental results on 12 well-known benchmark datasets demonstrate that our proposed framework achieves outstanding performance compared to state-of-the-art approaches for mitigating unfairness in terms of accuracy as well as 25 fairness measures although only a few of them were dynamically selected and used as optimisation objectives. The results indicate the importance of setting optimisation objectives dynamically during training. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

Talking Face Generation via Learning Semantic and Temporal Synchronous Landmarks

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Talking Face Generation via Learning Semantic and Temporal S...

International Conference on Pattern Recognition

作者： Aihua Zheng Feixia Zhu Hao Zhu Mandi Luo Ran He Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Heifei China Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) CASIA Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Beijing China

ISBN: (纸本)9781728188089;9781728188096

Given a speech clip and facial image, the goal of talking face generation is to synthesize a talking face video with accurate mouth synchronization and natural face motion. Recent progress has proven the effectiveness of the landmarks as the intermediate information during talking face generation. However, the large gap between audio and visual modalities makes the prediction of landmarks challenging and limits generation ability. This paper proposes a semantic and temporal synchronous landmark learning method for talking face generation. First, we propose to introduce a word detector to enforce richer semantic information. Then, we propose to preserve the temporal synchronization and consistency between landmarks and audio via the proposed temporal residual loss. Lastly, we employ a U-Net generation network with adaptive reconstruction loss to generate facial images for the predicted landmarks. Experimental results on two benchmark datasets LRW and GRID demonstrate the effectiveness of our model compared to the state-of-the-art methods of talking face generation.

关键词： Learning systems Visualization Face recognition Semantics Mouth Detectors Benchmark testing

Effectiveness of Neurofeedback-Assisted and Conventional 6-Week Web-Based Mindfulness Interventions on Mental Health of Chinese Nursing Students: Randomized Controlled Trial

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Journal of medical Internet research 2025年 27卷 e71741页

作者： Shu Jing Zhenwei Dai Xiaoyang Liu Xuelin Yang Jinglei Cheng Tianming Chen Zihang Feng Xin Liu Fenghe Dong You Xin Zhuoyan Han Haiyan Hu Xiaoyou Su Chen Wang School of Population Medicine and Public Health Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China. Peking University Sixth Hospital Peking University Beijing China. Peking University Institute of Mental Health Peking University Beijing China. National Health Commission Key Laboratory of Mental Health Peking University Beijing China. National Clinical Research Center for Mental Disorders Peking University Sixth Hospital Beijing China. School of Nursing and Institute of Nursing Research School of Medicine Zhejiang University Hangzhou China. School of Nursing Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China. College of Computer Science and Technology Zhejiang University Hangzhou China. The State Key Laboratory of Brain-machine Intelligence Zhejiang University Hangzhou China. School of Translation and Interpreting Beijing Language and Culture University Beijing China. Shenzhen Maternity and Child Healthcare Hospital Southern Medical University Shenzhen China. State Key Laboratory of Respiratory Health and Multimorbidity Beijing China.

BACKGROUND:Nursing students experience disproportionately high rates of mental health challenges, underscoring the urgent need for innovative, scalable interventions. Web-based mindfulness programs, and more recently, neurofeedback-enhanced approaches, present potentially promising avenues for addressing this critical issue. OBJECTIVE:This study aimed to explore the effectiveness of the neurofeedback-assisted online mindfulness intervention (NAOM) and the conventional online mindfulness intervention (COM) in reducing mental health symptoms among Chinese nursing students. METHODS:A 3-armed randomized controlled trial was conducted among 147 nursing students in Beijing, China, using a 6-week web-based mindfulness program. Participants received NAOM, COM, or general mental health education across 6 weeks. Electroencephalogram and validated tools such as the Patient Health Questionnaire and the Generalized Anxiety Disorder Questionnaire were used to primarily assess symptoms of depression and anxiety at baseline, immediately after the intervention, and at 1 and 3 months after the intervention. Generalized estimating equations were used to evaluate the effects of intervention and time. RESULTS:A total of 155 participants enrolled in the study, and 147 finished all assessments. Significant reductions in the symptoms of depression, anxiety, and fatigue were observed in the NAOM (mean difference [MD]=-3.330, Cohen d=0.926, P<.001; MD=-3.468, Cohen d=1.091, P<.001; MD=-2.620, Cohen d=0.743, P<.001, respectively) and the COM (MD=-1.875, Cohen d=0.490, P=.03; MD=-1.750, Cohen d=0.486, P=.02; MD=-2.229, Cohen d=0.629, P=.01, respectively) groups compared with the control group at postintervention assessment. Moreover, the NAOM group showed significantly better effects than the COM group in alleviating depressive symptoms (MD=-1.455; Cohen d=0.492; P=.04) and anxiety symptoms (MD=-1.718; Cohen d=0.670; P=.04) and improving the level of mindfulness (MD=-3.765; Cohen d=1.245; P<.0

关键词： anxiety depression neurofeedback nursing student mental health online mindfulness randomized controlled trial