用高维计算实现概念空间中的类比推理，突破传统逻辑框架。
Analogical Reasoning Within a Conceptual Hyperspace
- 结合高维计算与概念空间理论，构建类比推理架构
- 在简化场景中验证了类别与属性类比的可行性
- 为认知型AI提供可操作的类比推理新路径
我们提出一种类比推理方法，将复数采样的高维计算（HDC）的神经符号计算能力与概念空间理论（CST）相结合。CST从抽象层面提出了超越传统谓词结构映射理论的类比推理方式，但未说明如何实现。本文提出一种基于HDC的具体架构，可计算由CST分类的多种类比类型。在简化领域中展示了初步概念验证结果，说明该方法能够实现基于类别的和基于属性的类比推理。
原文摘要 · Abstract (English)
We propose an approach to analogical inference that marries the neuro-symbolic computational power of complex-sampled hyperdimensional computing (HDC) with Conceptual Spaces Theory (CST), a promising theory of semantic meaning. CST sketches, at an abstract level, approaches to analogical inference that go beyond the standard predicate-based structure mapping theories. But it does not describe how such an approach can be operationalized. We propose a concrete HDC-based architecture that computes several types of analogy classified by CST. We present preliminary proof-of-concept experimental results within a toy domain and describe how it can perform category-based and property-based analogical reasoning.
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