Adapting A Vector-Symbolic Memory for Lisp ACT-R
This work incrementally improves cognitive architecture tools for researchers by making vector-symbolic memory compatible with a widely-used ACT-R implementation.
The researchers adapted Holographic Declarative Memory (HDM), a vector-symbolic alternative to ACT-R's Declarative Memory, to work with Lisp ACT-R, enabling existing ACT-R models to run with HDM with minimal changes. They developed vector-based functions, a text processing pipeline, and a novel retrieval mechanism, maintaining HDM's advantages like scalability while requiring little modification to models.
Holographic Declarative Memory (HDM) is a vector-symbolic alternative to ACT-R's Declarative Memory (DM) system that can bring advantages such as scalability and architecturally defined similarity between DM chunks. We adapted HDM to work with the most comprehensive and widely-used implementation of ACT-R (Lisp ACT-R) so extant ACT-R models designed with DM can be run with HDM without major changes. With this adaptation of HDM, we have developed vector-based versions of common ACT-R functions, set up a text processing pipeline to add the contents of large documents to ACT-R memory, and most significantly created a useful and novel mechanism to retrieve an entire chunk of memory based on a request using only vector representations of tokens. Preliminary results indicate that we can maintain vector-symbolic advantages of HDM (e.g., chunk recall without storing the actual chunk and other advantages with scaling) while also extending it so that previous ACT-R models may work with the system with little (or potentially no) modifications within the actual procedural and declarative memory portions of a model. As a part of iterative improvement of this newly translated holographic declarative memory module, we will continue to explore better time-context representations for vectors to improve the module's ability to reconstruct chunks during recall. To more fully test this translated HDM module, we also plan to develop decision-making models that use instance-based learning (IBL) theory, which is a useful application of HDM given the advantages of the system.