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学者姓名：陈勃
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Abstract ：
Recent years have witnessed continuous optimization and innovation of reinforcement learning algorithms. Games, as a key application paradigm, have been widely employed to develop superior reinforcement learning models. Different game environments present diverse challenges to reinforcement learning agents; however, mainstream gaming paradigms have not yet specifically addressed issues such as variable action spaces and varying tasks. In this regard, this paper introduces a two-player adversarial game with a configurable player action space. The game allows for the diversification of task challenges by configuring opponent strategies. Additionally, we propose a reinforcement learning method to facilitate the decision-making of the AI player (the agent) in the game. The inclusion of an action masking algorithm enables effective handling of variable action space issues. Experimental results indicate that the decision-making behavior of the agent adjusts with changes in opponent behavior and continuously improves with policy updates. The trained agent exhibits impressive performance in this game, it shows that the proposed method could serve as a baseline for the decision-making in the novel game, and a robust foundation for further research and applications is provided. © 2024 IEEE.
Keyword ：
Decision making Decision making Intelligent agents Intelligent agents Learning algorithms Learning algorithms Learning systems Learning systems Multi agent systems Multi agent systems Optimization Optimization Reinforcement learning Reinforcement learning
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| GB/T 7714 | Chen, Bo , Lin, Ziqing , Chen, Guohong . Coins Game - A Novel Decision Problem and Its Solving Using Reinforcement Learning [C] . 2024 : 143-148 . |
| MLA | Chen, Bo 等. "Coins Game - A Novel Decision Problem and Its Solving Using Reinforcement Learning" . (2024) : 143-148 . |
| APA | Chen, Bo , Lin, Ziqing , Chen, Guohong . Coins Game - A Novel Decision Problem and Its Solving Using Reinforcement Learning . (2024) : 143-148 . |
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Multi-agent simulation based on deep reinforcement learning can simulate realistic economic agents well, and can play an important role in research work on complex economic system. In the simulated economic system model, DQN is used to handle the decision-making of economic agents, and their behavior strategies are continuously improved with the accumulation of experience as they interact with the environment. However, the action selector used by economic agents can affect their policy changes. Taking this problem into consideration, this paper did research on the action selector used by economic agents. Through simulation experiments, we compare the behavior policy of economic agents using different action selectors including the traditional -greedy and a novel NoisyNet method. The results show that an appropriate action selector enables economic agents to explore more widely and comprehensively, and learn a better policy. © 2021 IEEE.
Keyword ：
Decision making Decision making Deep learning Deep learning Learning systems Learning systems Multi agent systems Multi agent systems Reinforcement learning Reinforcement learning
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| GB/T 7714 | Chen, Bo , Wang, Sijie , Pei, Hong . The Action Selector in the Deep Q-learning Applied in a Multi-agent Economic System [C] . 2021 : 41-45 . |
| MLA | Chen, Bo 等. "The Action Selector in the Deep Q-learning Applied in a Multi-agent Economic System" . (2021) : 41-45 . |
| APA | Chen, Bo , Wang, Sijie , Pei, Hong . The Action Selector in the Deep Q-learning Applied in a Multi-agent Economic System . (2021) : 41-45 . |
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The development of artificial intelligence has brought new methods for botnet detection. For better performance, deep learning (DL) is more and more widely employed to botnet detecting. The existing DL-based botnet detection methods require lots of computing resources and running time. While in the real Internet of Things (IoT) environment, real-time and low computing consumption are much needed. Therefore, the DL-based methods seem to be powerless in real-time IoT scenarios. For these reasons, this article proposes a botnet detection model based on extreme learning machine, named BotDetector, which can directly obtain network stream files and quickly learn without data processing to extract botnet traffic characteristics. Experiments show that BotDetector has a good performance, which can identify botnets accurately with great reduction the time consumption and resource consumption. Furthermore, BotDetector has strong applicability in real IoT scenes.
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| GB/T 7714 | Dong, Xudong , Dong, Chen , Chen, Zhenyi et al. BotDetector: An extreme learning machine-based Internet of Things botnet detection model [J]. | TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES , 2020 . |
| MLA | Dong, Xudong et al. "BotDetector: An extreme learning machine-based Internet of Things botnet detection model" . | TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES (2020) . |
| APA | Dong, Xudong , Dong, Chen , Chen, Zhenyi , Cheng, Ye , Chen, Bo . BotDetector: An extreme learning machine-based Internet of Things botnet detection model . | TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES , 2020 . |
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Abstract ：
As people's living standards improve, the demand for resources is also increasing, especially power resources. This also puts high demands on the power sector: how to save power generation costs while effectively meeting people's electricity demand. This paper takes the gas-steam combined cycle power plant (CCPP) as the research object and uses the machine learning method to analyze the historical data of the power plant to find out the impact of the environment on the power generation efficiency. And establish a machine learning model to predict the net power generated by the power plant to help its intelligent work. The experimental results show that the machine learning model established in this paper can effectively predict the net electricity generated, and at the same time find the main factors affecting power generation, which can promote the improved production of power plants. © 2020 IEEE.
Keyword ：
Adaptive boosting Adaptive boosting Combined cycle power plants Combined cycle power plants Decision trees Decision trees Forecasting Forecasting Machine learning Machine learning Steam power plants Steam power plants Trees (mathematics) Trees (mathematics)
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| GB/T 7714 | Dong, Xudong , Dong, Chen , Chen, Bo et al. Application of AdaBoost Algorithm Based on Decision Tree in Forecasting Net power of Circulating Power Plants [C] . 2020 : 747-750 . |
| MLA | Dong, Xudong et al. "Application of AdaBoost Algorithm Based on Decision Tree in Forecasting Net power of Circulating Power Plants" . (2020) : 747-750 . |
| APA | Dong, Xudong , Dong, Chen , Chen, Bo , Zhong, Junliang , He, Guorong , Chen, Zhenyi . Application of AdaBoost Algorithm Based on Decision Tree in Forecasting Net power of Circulating Power Plants . (2020) : 747-750 . |
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Abstract ：
Although distributed systems are mostly used in internal environments, they are also inevitably exposed to some untrusted environments where some potential security problems exist. The TLS 1.3[1], a new network security standard, was officially released in August 2018. It is of great significance for the authentication and secure transmission of public keys. At the same time, it can be used as a reference for the implementation of the bi-directional authentication between the subsystems in distributed systems. Based on the principle and thought of TLS1.3, an improvement on the authentication subsystem is proposed, taking Ceph, a distributed storage system, as an example. This paper designed an improved solution for Ceph's communication in an insecure environment.
Keyword ：
authentication authentication Ceph Ceph encryption encryption key negotiation key negotiation
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| GB/T 7714 | Dai, Kangning , Chen, Bo . An Improvement on the Authentication and Encryption of Ceph in Unreliable Environments [C] . 2019 : 313-319 . |
| MLA | Dai, Kangning et al. "An Improvement on the Authentication and Encryption of Ceph in Unreliable Environments" . (2019) : 313-319 . |
| APA | Dai, Kangning , Chen, Bo . An Improvement on the Authentication and Encryption of Ceph in Unreliable Environments . (2019) : 313-319 . |
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Abstract ：
Although distributed systems are mostly used in internal environments, they are also inevitably exposed to some untrusted environments where some potential security problems exist. The TLS 1.3 [1], a new network security standard, was officially released in August 2018. It is of great significance for the authentication and secure transmission of public keys. At the same time, it can be used as a reference for the implementation of the bi-directional authentication between the subsystems in distributed systems. Based on the principle and thought of TLS1.3, an improvement on the authentication subsystem is proposed, taking Ceph, a distributed storage system, as an example. This paper designed an improved solution for Ceph's communication in an insecure environment. © 2019 IEEE.
Keyword ：
Authentication Authentication Cryptography Cryptography Distributed database systems Distributed database systems Multiprocessing systems Multiprocessing systems Network security Network security Software engineering Software engineering
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| GB/T 7714 | Dai, Kangning , Chen, Bo . An improvement on the authentication and encryption of ceph in unreliable environments [C] . 2019 : 309-315 . |
| MLA | Dai, Kangning et al. "An improvement on the authentication and encryption of ceph in unreliable environments" . (2019) : 309-315 . |
| APA | Dai, Kangning , Chen, Bo . An improvement on the authentication and encryption of ceph in unreliable environments . (2019) : 309-315 . |
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针对深度Q网络(DQN)应用中基于python数据结构直接实现的经验回放过程时常成为性能瓶颈,提出一种具有高性能及通用性的经验回放模块设计方案.该设计方案具有两层软件结构:底层的功能内核由C++语言实现,以提供较高的执行效率;上层则由python语言编写,以面向对象的方式封装模块功能并提供调用接口,使模块具有较高易用性.针对经验回放所涉及的关键操作,一些技术细节被充分研究和精心设计,例如,将优先级回放机制作为附属组件与模块的主体运行逻辑分离,将样本的可抽取性验证提前到样本记录操作中进行,使用高效的样本淘汰策略与算法等.这些措施使模块具有较高的通用性和可扩展性.实验结果表明,按照该模块实现的经验回放过程,整体执行效率得到了充分优化,两个关键操作——样本记录与样本抽取,皆可高效执行.与基于python数据结构的直接实现方式相比,所提模块在样本抽取操作上的性能提升了约100倍,从而避免了经验回放过程成为整个系统的性能瓶颈,满足了各类DQN相关应用项目的需要.
Keyword ：
强化学习 强化学习 深度Q网络 深度Q网络 深度学习 深度学习 经验回放 经验回放 软件设计 软件设计
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| GB/T 7714 | 陈勃 , 王锦艳 . 一种高效的经验回放模块设计 [J]. | 计算机应用 , 2019 , 39 (11) : 3242-3249 . |
| MLA | 陈勃 et al. "一种高效的经验回放模块设计" . | 计算机应用 39 . 11 (2019) : 3242-3249 . |
| APA | 陈勃 , 王锦艳 . 一种高效的经验回放模块设计 . | 计算机应用 , 2019 , 39 (11) , 3242-3249 . |
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For the problems related to market equilibrium in complex market environments, analyses are conducted in the past, using some mathematical models and the game theory. These methods are based on the economic structural equations themselves, ignoring the interactions between economic subjects, and the hypothesis of subject homogeneity has no reference in the real world. On contrast, this paper proposes a multi-agent simulation model, from the microscopic point of view. In such simulation, agents interact with each other, and the decisions are made by agent-embedded AI systems, the Q-network. Therefore, there is no need to elaborate the behavioral rule for each agent, or manually set up too many assumptions. This paper assumes that the simulated market operates in a hypothetical way, in which there are two types of economic entities, namely, banks and enterprises. Banks and enterprises lending behaviors lead to a symbiotic relationship between the banks and the enterprises, while business-to-business transactions make the enterprises symbiotically compete with each other. In the experiment, the observed behavior of each agent can be reasonably explained. Agents endogenously generate intelligent behavioral patterns compatible with the environment. Therefore, this AI-based method can replace the artificially designated decision-making strategy in simulations of market, thus facilitating related economic researches. © 2019 IEEE.
Keyword ：
Artificial intelligence Artificial intelligence Behavioral research Behavioral research Commerce Commerce Decision making Decision making Embedded systems Embedded systems Game theory Game theory Markov processes Markov processes Multi agent systems Multi agent systems
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| GB/T 7714 | Wang, Jinyan , Chen, Bo . A Multi-agent Simulation for the Research on the Market Equilibrium Phenomena Using Q-Network Algorithm [C] . 2019 : 356-361 . |
| MLA | Wang, Jinyan et al. "A Multi-agent Simulation for the Research on the Market Equilibrium Phenomena Using Q-Network Algorithm" . (2019) : 356-361 . |
| APA | Wang, Jinyan , Chen, Bo . A Multi-agent Simulation for the Research on the Market Equilibrium Phenomena Using Q-Network Algorithm . (2019) : 356-361 . |
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本文提出一种基于注意力相关脑电分析的注意力状态识别方法,利用共同空间模式(CSP)滤波、通过自回归算法计算功率谱、进而采用统计分析选取差异明显的特征向量、并采用支持向量机(SVM)进行分类.实验表明,该方法可以有效分类注意力状态.
Keyword ：
共同空间模式 共同空间模式 支持向量机 支持向量机 脑机接口 脑机接口
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| GB/T 7714 | 马文鸿 , 陈勃 . 注意力状态识别方法研究 [J]. | 福建电脑 , 2017 , 33 (10) : 16-17 . |
| MLA | 马文鸿 et al. "注意力状态识别方法研究" . | 福建电脑 33 . 10 (2017) : 16-17 . |
| APA | 马文鸿 , 陈勃 . 注意力状态识别方法研究 . | 福建电脑 , 2017 , 33 (10) , 16-17 . |
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Abstract ：
本发明涉及一种脑眼混合驱动的家电控制方法。该控制方法允许用户仅依靠注意力影响下的脑电和眼睛注视移动向系统传递控制命令；实现该方法的系统有三个状态：启动状态、电器选择状态、键盘选择状态；用户在系统的不同状态下，通过集中注意力注视相应目标来表达自身意愿，所述系统检测相关的信号来决定系统在这三个状态间的切换，同时合理设置系统待命、回退等相关操作完善状态的切换。本发明提供的方法能够直接利用大脑和眼睛实现对家电设备的控制。
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| GB/T 7714 | 黄志华 , 陈勃 , 马文鸿 et al. 脑眼混合驱动的家电控制方法 : CN201710499330.3[P]. | 2017/6/27 . |
| MLA | 黄志华 et al. "脑眼混合驱动的家电控制方法" : CN201710499330.3. | 2017/6/27 . |
| APA | 黄志华 , 陈勃 , 马文鸿 , 黄炜 , 王小娜 , 林智锋 . 脑眼混合驱动的家电控制方法 : CN201710499330.3. | 2017/6/27 . |
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