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Extending cognitive architectures with spatial and visual imagery mechanisms.

机译:利用空间和视觉图像机制扩展认知体系。

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摘要

This research presents a computational synthesis of cognition with spatial and visual imagery processing by extending a symbolic cognitive architecture (Soar) with mechanisms to support reasoning with quantitative spatial and visual depictive representations. Inspired by psychological and neurological evidence of mental imagery, our primary goals are to achieve new functional capability and computational efficiency in a task-independent manner. We describe how our theory and the corresponding architecture derive from behavioral, biological, functional, and computational constraints and demonstrate results from three different domains. Our evaluation reveals that in tasks where reasoning includes many spatial or visual properties, the combination of amodal and perceptual representations provides an agent with additional functional capability and improves its problem-solving quality. We also show that specialized processing units specific to a perceptual representation but independent of task knowledge are likely to be necessary in order to realize computational efficiency in a general manner.;The research is significant because past research in cognitive architectures primarily views amodal, symbolic representations as being sufficient for knowledge representation and thought. We expand those ideas with the notion that perceptual-based representations participate directly in the thinking rather than serving simply as a source of sensory information. The new capabilities of the resulting architecture, which includes Soar and its Spatial-Visual Imagery (SVI) component, emerge from its ability to amalgamate symbolic and perceptual representations and use them to inform reasoning. Soar's symbolic memories and processes provide the building blocks necessary for high-level control in the pursuit of goals, learning, and the encoding of amodal, symbolic knowledge for abstract reasoning. SVI encompasses the quantitative spatial and visual depictive representations and processes specialized for efficient construction and extraction of spatial and visual properties.
机译:这项研究通过扩展符号认知架构(Soar)以及支持定量的空间和视觉描绘表示推理的机制,提出了一种具有空间和视觉图像处理功能的认知计算综合。受心理意象的心理和神经学证据启发,我们的主要目标是以独立于任务的方式获得新的功能和计算效率。我们描述了我们的理论和相应的体系结构是如何从行为,生物学,功能和计算约束中得出的,并展示了来自三个不同领域的结果。我们的评估表明,在推理包含许多空间或视觉特性的任务中,无情态表示法和感性表示法的组合为代理提供了附加的功能能力,并提高了其解决问题的质量。我们还表明,为了以一般方式实现计算效率,特定于感知表示但独立于任务知识的专门处理单元可能是必要的;该研究意义重大,因为过去在认知体系结构中的研究主要看待无模态,象征性表示足以代表知识和思想。我们以基于感知的表示直接参与思想而不是简单地作为感觉信息的来源这一概念来扩展这些思想。最终架构的新功能(包括Soar及其空间视觉图像(SVI)组件)源自其融合符号和感知表示并使用它们进行推理的功能。 Soar的象征性记忆和过程为追求目标,学习和对抽象推理进行无模态,象征性知识的编码提供了高层控制所必需的构造块。 SVI包含定量的空间和视觉描绘表示和过程,专门用于有效构造和提取空间和视觉特性。

著录项

  • 作者

    Lathrop, Scott D.;

  • 作者单位

    University of Michigan.;

  • 授予单位 University of Michigan.;
  • 学科 Artificial Intelligence.;Computer Science.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 197 p.
  • 总页数 197
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;自动化技术、计算机技术;
  • 关键词

  • 入库时间 2022-08-17 11:38:43

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