Learning from Interpretation Transition (LFIT) is a method of unsupervised learning, which learns the dynamics of a system from observed time-series data. LFIT has been developed in three ways: (1) Learning memory-less systems from 1-step state transitions, which contains three different implementations to learn the state transition rules from a series of 1-step transitions of system states, that is, (a) generalization based on the resolution principle, (b) extension of the binary decision

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... am (BDD), and (c) least specialization that guarantees the minimality of learned rules; (2) Learning systems with memory (or delay), which can learn Markov(k) systems that depend on k previous states; and (3) Learning nondeterministic and probabilistic systems, which can work for noisy data. These three learning algorithms have been implemented and evaluated with bioinformatics data to construct gene regulatory networks. LFIT has also been applied to identification of cellular automata, learning robot planning rules, and learning logics.