Assessing a Sparse Distributed Memory Using Different Encoding Methods
Mateus Mendes, A. Paulo Coimbra, Manuel M. Crisostomo · 2009
a kind of associative memory suitable to work with high-dimensional vectors of random data. This memory model is attractive for Robotics and Artificial Intelligence, for it is able to mimic many characteristics of the human long-term memory. However, sensorial data is not always random: most of the times it is based on the Natural Binary Code (NBC) and tends to cluster around some specific points. This means that the SDM performs poorer than expected. As part of an ongoing project, in which we intend to navigate a robot using a sparse distributed memory to store sequences of sensorial information, we tested different methods of encoding the data. Some methods perform better than others, though some may fade particular characteristics present in the original SDM model.