弹性监管多代理系统 弹性监管多代理系统
Kleio Baxevani1, Ashkan Zehfroosh1, Herbert G Tanner1
1Department of Mechanical Engineering, University of Delaware.
概括
本研究介绍了一种机器学习方法,使多代理系统能够抵御领导者失败. 代理人自主学习角色,确保协调中断后的操作连续性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络科学 网络科学
背景情况:
- 多代理系统 (MAS) 容易受到中央协调中断的影响,称为"领导者切断".
- 在此类故障后恢复正常运行对于系统可靠性至关重要.
研究的目的:
- 开发一种方法来增强多代理网络对协调功能失效的弹性.
- 通过机器学习,能够及时恢复正常运行.
主要方法:
- 代理人配备了独立的学习模块,可以在系统的协调战略中发现角色.
- 当中央协调停止时,机器学习算法可以促进自主战略实施.
- 代理人逐步确定系统任务规范,并为共同目标优化个人策略.
主要成果:
- 展示了一个创建弹性多代理系统的方法.
- 展示了代理人自主适应和维持首后系统功能的能力.
- 验证了机器学习在分散协调恢复中的有效性.
结论:
- 拟议的机器学习方法显著提高了多代理网络对领导者首的弹性.
- 代理人的自主角色发现和战略优化确保了运营连续性.
- 这种方法为在去中心化网络中保持系统功能提供了强大的解决方案.
相关概念视频
Masking and Demasking Agents
2.4K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.4K
Avoidance Learning and Learned Helplessness
1.7K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
1.7K
Multimachine Stability
151
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
151
Reinforcement
202
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
202
Observational Learning
166
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
166
Stereotype Content Model
14.7K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.7K


