Evolutionary learning in complex social networks

复杂社交网络中的进化学习

• 批准号: 327482-2006
• 负责人: Kobti, Ziad
• 金额: $ 0.95万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=361724
• 关键词: Evolutionary; learning; complex; social; networks

Complex systems and agent-based modelling are attracting a growing interest from experts from various disciplines to simulate and study real world applications. The implications are of significance because they broaden our understanding of little known complex social phenomena, including reasoning about the causes of observed system behaviour and predicting the outcomes of tested case scenarios or interventions. Exciting systems that this field encompasses include: terrorist networks, prehistoric civilizations, and vehicle safety. An agent for instance can represent an individual, a household, or a driver. System designers often build a state based configuration for agents to react in a dynamic environment. Hence, the model provides a sandbox approach for the researchers to test various hypotheses. For example, an ancient civilization can be modelled using real field data where an archaeologist can then examine the effects on the emerging population size and movement under drought conditions compared to normal conditions in order to test a hypothesis suggesting that a drought caused the population to leave the study area. The research program proposes applying new techniques in agent learning when developing agent behaviour in a complex system. Beyond the basic finite state machine that an agent can use to react to its dynamic environment, a more realistic approach for agent intelligence proposes reactive agents that can accumulate knowledge at the individual and population levels, learn new plans and evolve new strategies on their own to fulfill their goals. Human behaviour however, is distinguished from other animal behaviours and consequently the conventional evolutionary frameworks need to be extended in order to build more realistic artificial agents. Through the proposed methods, the outcome of this research program will enhance the learning theory in artificial models and researchers working with agent-based simulations would be able to create more accurate and intelligent models using the algorithms from this work.

复杂系统和基于代理的建模吸引了来自不同学科的专家越来越多的兴趣来模拟和研究真实的世界应用。这些影响具有重要意义，因为它们拓宽了我们对鲜为人知的复杂社会现象的理解，包括对观察到的系统行为的原因进行推理，以及预测测试案例场景或干预措施的结果。这个领域包含的令人兴奋的系统包括：恐怖分子网络，史前文明和车辆安全。例如，代理可以代表个人、家庭或司机。系统设计者通常为代理构建基于状态的配置，以在动态环境中做出反应。因此，该模型为研究人员提供了一个沙箱方法来测试各种假设。例如，可以使用真实的实地数据对古代文明进行建模，然后考古学家可以检查干旱条件下与正常条件下相比对新兴人口规模和运动的影响，以测试表明干旱导致人口离开研究区域的假设。该研究计划提出在复杂系统中开发代理行为时，在代理学习中应用新技术。除了代理可以用来对其动态环境做出反应的基本有限状态机之外，一种更现实的代理智能方法提出了反应式代理，可以在个人和群体层面积累知识，学习新计划并自行发展新策略以实现其目标。然而，人类行为与其他动物行为不同，因此需要扩展传统的进化框架，以构建更现实的人工代理。通过提出的方法，这项研究计划的成果将增强人工模型中的学习理论，研究人员使用基于代理的模拟将能够使用这项工作的算法创建更准确和智能的模型。