Sparse distributed memory for ‘conscious’ software agents

In this work we are reporting a case study on the use of SDM as the associative memory for a software agent, CMattie, whose architecture is modeled on human cognition. Sparse distributed memory (SDM) is a content-addressable memory technique that relies on close memory items tending to be clustered together. In this work, we used an enhanced version of SDM augmented with the use of genetic algorithms as an associative memory in our ‘conscious’ software agent, CMattie, who is responsible for emailing seminar announcements in an academic department. Interacting with seminar organizers via email in natural language, CMattie can replace the secretary who normally handles such announcements. SDM is a key ingredient in a complex agent architecture that implements global workspace theory, a psychological theory of consciousness and cognition. In this architecture, SDM, as the primary memory for the agent, provides associations with incoming percepts. These include disambiguation of the percept by removing noise, correcting misspellings, and adding missing pieces of information. It also retrieves behaviors and emotions associated with the percept. These associations are based on previous similar percepts, and their consequences, that have been recorded earlier. SDM also possesses several key psychological features. Some enhancements to SDM including multiple writes of important items, use of error detection and correction, and the use of hashing to map the original information into fixed size keys were used. Test results indicate that SDM can be used successfully as an associative memory in such complex agent architectures. The results show that SDM is capable of recovering a percept based on a part of that percept, and finding defaults for empty perception registers. The evaluation of suggested actions and emotional states is satisfactory. We think that this work opens the door to more scientific and empirical uses for SDM.