Machine Learning

Scale-invariant temporal history (SITH): optimal slicing of the past in an uncertain world

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  • arXiv
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    Scale-invariant temporal history (SITH): optimal slicing of the past in an uncertain world

    In both the human brain and any general artificial intelligence (AI), a representation of the past is necessary to predict the future. However, perfect storage of all experiences is not possible. One possibility, utilized in many applications, is to retain information about the past in a buffer. A limitation of this approach is that although events in the buffer are represented with perfect accuracy, the resources necessary to represent information at a particular time scale go up rapidly. Here we present a neurally-plausible, compressed, scale-free memory representation we call Scale-Invariant Temporal History (SITH). This representation covers an exponentially large period of time in the past at the cost of sacrificing temporal accuracy for events further in the past. The form of this decay is scale-invariant and can be shown to be optimal in that it is able to respond to worlds with a wide range of time scales. We demonstrate the utility of this representation in learning to play a simple video game. In this environment, SITH exhibits better learning performance than a fixed-size buffer history representation. Whereas the buffer performs well as long as the temporal dependencies can be represented within the buffer, SITH performs well over a much larger range of time scales for the same amount of resources. Finally, we discuss how the application of SITH, along with other human-inspired models of cognition, could improve reinforcement and machine learning algorithms in general.

    Scale-invariant temporal history (SITH): optimal slicing of the past in an uncertain world
    by Tyler A. Spears, Brandon G. Jacques, Marc W. Howard, Per B. Sederberg
    https://arxiv.org/pdf/1712.07165v1.pdf

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