Abstract: A method for temporal logic fusion can include steps of: receiving a plurality of inputs for a plurality of behavior classes, the inputs consisting of single- or multi-dimensional states sampled over time; computing a distance metric pairwise among the inputs, the computing being performed using dynamic time warping; mapping the high-dimension input signals into a 2-dimensional (2-D) space using t-distributed Stochastic Neighbor Embedding, the pairwise computation from the computing step being used as the distance metric required to perform this mapping; clustering the high-dimension input signals in the 2-D space via a k-means clustering algorithm; generating a signal temporal logic (STL) expression that distinguishes between a cluster in a behavior class and all high-dimension input signals not in that behavior class; and repeating the generating step for each cluster in that behavior class.

Abstract: A method for temporal logic fusion can include steps of: receiving a plurality of inputs for a plurality of behavior classes, the inputs consisting of single- or multi-dimensional states sampled over time; computing a distance metric pairwise among the inputs, the computing being performed using dynamic time warping; mapping the high-dimension input signals into a 2-dimensional (2-D) space using t-distributed Stochastic Neighbor Embedding, the pairwise computation from the computing step being used as the distance metric required to perform this mapping; clustering the high-dimension input signals in the 2-D space via a k-means clustering algorithm; generating a signal temporal logic (STL) expression that distinguishes between a cluster in a behavior class and all high-dimension input signals not in that behavior class; and repeating the generating step for each cluster in that behavior class.