---
title: "Self-Managing Associative Memory for Dynamic Acquisition of Expertise in High-Level Domains"
authors:
  - "Jacob Beal"
doi: ""
url: "https://ijcai.org/proceedings/2009/169"
pdf: "https://ijcai.org/proceedings/2009/0169.pdf"
venue: "Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence"
year: 2009
pages: "998-1002"
track: ""
keywords:
---

# Self-Managing Associative Memory for Dynamic Acquisition of Expertise in High-Level Domains

Jacob Beal

*Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence*, pages 998-1002.

## Abstract

Self-organizing maps can be used to implement an associative memory for an intelligent system that dynamically learns about new high-level domains over time. SOMs are an attractive option for implementing associative memory: they are fast, easily parallelized, and digest a stream of incoming data into a topographically organized collection of models where more frequent classes of data are represented by higher-resolution collections of models. Typically, the distribution of models in an SOM, once developed, remains fairly stable, but developing expertise in a new high-level domain requires altering the allocation of models. We use a mixture of analysis and empirical studies to characterize the behavior of SOMs for high-level associative memory, finding that new high-resolution collections of models develop quickly. High-resolution areas of the SOM decay rapidly unless actively refreshed, but in a large SOM, the ratio between growth rate and decay rate may be high enough to support both fast learning and long-term memory.Jacob Beal