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首页> 外文学位 >Learning in dynamic hierarchical network structures in complex systems: A study on the effect of learning in improving resilience in large scale social networks.
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Learning in dynamic hierarchical network structures in complex systems: A study on the effect of learning in improving resilience in large scale social networks.

机译：复杂系统中动态分层网络结构中的学习：学习在提高大型社交网络的弹性中的作用。

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摘要

A Cultural Algorithm (CA) framework was developed for the Mesa Verde Village multi-agent simulation in Swarm. Going beyond the basic farming resources of agents, we implemented two main social networks: a kinship relation network for generalized reciprocal exchange, and an economic network for balanced reciprocal exchange. Agents, or households, are able to procure several resources. They include agriculture, the hunting of deer, rabbits, and hares, the collection of wood for fuel, and acquisition of water. Individuals can exchange surplus good for needed goods through the exchange network.; Intelligent artifacts that describe the attributes of an agent as they participate in these networks can be produced and reside in the cultural space. One such intelligent artifact that can be produced relates to an individual agents' reputation. Reputation can be modified through the individuals' participation in each of the two exchange networks. Acquired reputation can be used to determine how new communal networks form within the community. Agents can learn how to interact with others within the network based upon reputation as well as other factors. Agents can learn both procurement strategies in conjunction with exchange strategies in order to survive.; The agent can learn an individual procurement and exchange strategy based on its own experience under dynamic environmental conditions. The result of individual learning can be generalized at a global level using a Cultural Algorithm. The impact that these new extensions to the model have on the systems' overall resiliency and reliability are then examined and compared to earlier versions without these additional features. Overall, the emerging communities overtime will attain certain measured levels of quality of life. Low quality of life will present motive for the population to move outside the study area.
机译：针对Swarm中的Mesa Verde Village多智能体仿真开发了文化算法（CA）框架。超越代理商的基本农业资源，我们实现了两个主要的社交网络：用于广义互惠交易的亲属关系网络和用于平衡互惠交易的经济网络。代理商或家庭可以购买多种资源。其中包括农业，猎鹿，兔子和野兔，收集用作燃料的木材以及获取水。个人可以通过交易网络将剩余的货物换成所需的货物。可以生成描述代理人参与这些网络时的属性的智能人工制品，并将其驻留在文化空间中。可以产生的一种此类智能工件与单个代理商的声誉有关。可以通过个人参与两个交换网络中的每一个来修改声誉。获得的声誉可用于确定社区中新的公共网络的形成方式。代理可以根据信誉以及其他因素来学习如何与网络中的其他人进行交互。代理商可以学习两种采购策略以及交换策略以求生存。代理商可以根据自己在动态环境条件下的经验来学习个人采购和交易策略。个体学习的结果可以使用文化算法在全球范围内进行概括。然后检查这些新的模型扩展对系统总体弹性和可靠性的影响，并将其与没有这些附加功能的早期版本进行比较。总体而言，新兴社区加班将达到一定程度的生活质量水平。生活质量低下将成为人口迁出研究区域的动力。

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  • 作者

    Kobti, Ziad.;

  • 作者单位

    Wayne State University.;

  • 授予单位 Wayne State University.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2004
  • 页码 286 p.
  • 总页数 286
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;
  • 关键词

  • 入库时间 2022-08-17 11:43:54

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