Vector Symbolic Architectures combine a high-dimensional vector space with a
set of carefully designed operators in order to perform symbolic computations
with large numerical vectors. Major goals are the exploitation of their
representational power and ability to deal with fuzziness and ambiguity. Over
the past years, several VSA implementations have been proposed. The available
implementations differ in the underlying vector space and the particular
implementations of the VSA operators. This paper provides an overview of eleven
available VSA implementations and discusses their commonalities and differences
in the underlying vector space and operators. We create a taxonomy of available
binding operations and show an important ramification for non self-inverse
binding operations using an example from analogical reasoning. A main
contribution is the experimental comparison of the available implementations in
order to evaluate (1) the capacity of bundles, (2) the approximation quality of
non-exact unbinding operations, (3) the influence of combining binding and
bundling operations on the query answering performance, and (4) the performance
on two example applications: visual place- and language-recognition. We expect
this comparison and systematization to be relevant for development of VSAs, and
to support the selection of an appropriate VSA for a particular task. The
implementations are available.

本文提出了 11 种可用的 VSA 实现，并讨论了它们在底层向量空间和操作符方面的相似性和差异性，并通过实验比较了它们的容量、非精确解绑操作的逼近质量、混合捆绑和绑定操作对查询响应性能的影响以及在两个示例应用程序中的性能。