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Contextualizing observational data for modeling human performance.

机译：对观察数据进行情境化以对人类绩效进行建模。

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摘要

This research focuses on the ability to contextualize observed human behaviors in efforts to automate the process of tactical human performance modeling through learning from observations. This effort to contextualize human behavior is aimed at minimizing the role and involvement of the knowledge engineers required in building intelligent Context-based Reasoning (CxBR) agents. More specifically, the goal is to automatically discover the context in which a human actor is situated when performing a mission to facilitate the learning of such CxBR models. This research is derived from the contextualization problem left behind in Fernlund's research on using the Genetic Context Learner (GenCL) to model CxBR agents from observed human performance [33]. To accomplish the process of context discovery, this research proposes two contextualization algorithms: Contextualized Fuzzy ART (CFA) and Context Partitioning and Clustering (COPAC). The former is a more naive approach utilizing the well known Fuzzy ART strategy while the latter is a robust algorithm developed on the principles of CxBR. Using Fernlund's original five drivers, the CFA and COPAC algorithms were tested and evaluated on their ability to effectively contextualize each driver's individualized set of behaviors into well-formed and meaningful context bases as well as generating high-fidelity agents through the integration with Fernlund's GenCL algorithm. The resultant set of agents was able to capture and generalized each driver's individualized behaviors.
机译：这项研究着重于将观察到的人类行为情境化的能力，以通过从观察中学习来使战术人类绩效建模过程自动化。这种将人类行为情境化的努力旨在最大程度地减少构建智能的基于情境的推理（CxBR）代理所需的知识工程师的作用和参与。更具体地说，目标是在执行任务以自动学习此类CxBR模型时自动发现人类演员所在的环境。这项研究源于Fernlund使用遗传背景学习器（GenCL）根据观察到的人类行为对CxBR代理进行建模的研究中遗留的背景问题[33]。为了完成上下文发现的过程，本研究提出了两种上下文化算法：上下文化模糊ART（CFA）和上下文分区与聚类（COPAC）。前者是利用众所周知的Fuzzy ART策略的一种较幼稚的方法，而后者是一种基于CxBR原理开发的健壮算法。使用Fernlund最初的五个驱动程序，对CFA和COPAC算法进行了测试和评估，以有效地将每个驱动程序的个性化行为情境化为格式正确且有意义的上下文基础，以及通过与Fernlund的GenCL算法集成来生成高保真代理的能力。生成的代理程序集能够捕获并概括每个驾驶员的个性化行为。

著录项

  • 作者

    Trinh, Viet.;

  • 作者单位

    University of Central Florida.;

  • 授予单位 University of Central Florida.;
  • 学科 Engineering Electronics and Electrical.;Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 836 p.
  • 总页数 836
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;人工智能理论;
  • 关键词

  • 入库时间 2022-08-17 11:38:29

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