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Inferring finite automata with stochastic output functions and an application to map learning
1995
Machine Learning
It is often useful for a robot to construct a spatial representation of its environment from experiments and observations, in other words, to learn a map of its environment by exploration. In addition, robots, like people, make occasional errors in perceiving the spatial features of their environments. We formulate map learning as the problem of inferring from noisy observations the structure of a reduced deterministic finite automaton. We assume that the automaton to be learned has a
doi:10.1007/bf00993822
fatcat:y7x26upvnnewtjxmfuotnixb2q