个性化模型驱动干预,用于从经验中做出决策
Edward A Cranford1, Christian Lebiere1, Cleotilde Gonzalez2
1Department of Psychology, Carnegie Mellon University.
Topics in cognitive science
|October 16, 2024
概括
这项研究将在认知架构中使用基于实例的学习 (IBL) 来模拟网络安全中的个人决策. 它展示了个性化模型如何适应不断变化的环境和个人差异,以获得更好的防御系统.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 知识的个体差异影响行为.
- 基于实例的学习理论 (IBLT) 模型基于经验的决策.
- 认知架构整合了学习和决策过程.
研究的目的:
- 展示一种基于实例的学习 (IBL) 认知模型,用于预测动态环境中的个体行为.
- 为了考虑人口平均值和决策中的个人差异.
- 为网络安全防御开发个性化的信号算法.
主要方法:
- 在思维理性的自适应控制 (ACT-R) 架构中实现IBL认知模型.
- 使用复发量化分析来检查连续的试验到试验行为.
- 应用模型追踪和知识追踪来实现实时个体对齐.
主要成果:
- 具有相同参数的IBL模型通过随机记忆检索生成了各种人类行为.
- 个性化建模成功与网络安全任务中的个人决策保持一致.
- 认知模型内观揭示了影响个人选择的突出特征.
结论:
- 结合的技术为个性化认知建模提供了一个蓝图.
- 这种适应性,个性化的方法对网络安全防御和智能系统有影响.
- 根据个体差异量身定制智能工件对于人机合作等领域至关重要.
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