Abstract
A Sparse Distributed Memory (SDM) is a kind of associative memory suitable to work with high-dimensional vectors of random data. This memory model exhibits the characteristics of a large boolean space, which are to a great extent those of the human long-term memory. Hence, this model is attractive for Robotics and Artificial Intelligence, since it can possibly grant artificial machines those same characteristics. However, the original SDM model is appropriate to work with random data. Sensorial data is not always random: most of the times it is based on the Natural Binary Code 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 the goal is to navigate a robot using a SDM to store and retrieve sequences of images and associated path information, different methods of encoding the data were tested. Some methods perform better than others, and one method is presented that can offer the best performance and still maintain the characteristics of the original model.
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Mendes, M., Crisóstomo, M.M., Coimbra, A.P. (2010). Encoding Data to Use with a Sparse Distributed Memory. In: Ao, SI., Gelman, L. (eds) Electronic Engineering and Computing Technology. Lecture Notes in Electrical Engineering, vol 60. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8776-8_25
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DOI: https://doi.org/10.1007/978-90-481-8776-8_25
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