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We aim to develop a transformative computational method for automatic discovery of a set of dynamical rules that best captures both state transition and topological transformation in the data of spatio-temporal evolution of a complex network.
Our goal is to develop a full working solver for ATA (with 1 clock) in Python, with MTL to ATA support. The decidability for the emptiness problem was proposed by Lasota and Walukiewicz. The MTL to ATA was proposed by Ouaknine and Worrell.