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.
@article{arxiv.2411.08684,
title = {Analogical Reasoning Within a Conceptual Hyperspace},
author = {Howard Goldowsky and Vasanth Sarathy},
journal= {arXiv preprint arXiv:2411.08684},
year = {2024}
}