Method and system for an intelligent artificial agent

An artificial intelligence system, comprising:

• a computing device including at least one processor, one or more data storage devices, and a non-transitory data storage medium interfaced with the at least one processor, the non-transitory data storage medium containing instructions that, when executed cause the at least one processor to:
• save observed sensory sequence information in a plurality of history windows, wherein a size of the plurality of history windows increase exponentially from a last observed time step;
• apply a function to the observed sensory sequence information in each history window, wherein the function maps the observed sensory sequence information into a fixed set of discrete classes, fixed for all of the plurality of history windows;
• choose at least one parameter of the exponentially increasing history window size as a hyperparameter to allow the system to trade off bias-variance;
• and
• apply a context tree weighting algorithm to an alphabet resulting from the fixed set of discrete classes for each of the plurality of history windows to predict a future discrete sequence;
• and
• use a deep neural network classifier to map arbitrary length histories to a minimal output symbol alphabet as an input sequence for the context tree weighting algorithm.

An artificial intelligence system, comprising:

• a computing device including at least one processor, one or more data storage devices, and a non-transitory data storage medium interfaced with the at least one processor, the non-transitory data storage medium containing instructions that, when executed cause the at least one processor to:
• save observed sensory sequence information in a plurality of history windows, wherein a size of the plurality of history windows increase exponentially from a last observed time step;
• apply a function to the observed sensory sequence information in each history window, wherein the function maps the observed sensory sequence information into a fixed set of discrete classes, fixed for all of the plurality of history windows;
• choose at least one parameter of the exponentially increasing history window size as a hyperparameter to allow the system to trade off bias-variance;
• apply a context tree weighting algorithm to an alphabet resulting from the fixed set of discrete classes for each of the plurality of history windows to predict a future discrete sequence;
• and
• perform a temporal convolution in a deep neural network to map observed sensory sequence information from the plurality of history windows to symbols, wherein
• the temporal convolution includes defining each of the plurality of history windows of events as a ($2{circumflex over ( )}k$)-by-n matrix, where $2{circumflex over ( )}k$ is a number of time steps in each of the plurality of history windows and n is a number of events at each of the time steps, and applying a convolution that is an l-by-n matric, where l is a small number, wherein a set of the convolutions produces a new set of events.