Efficient learning of real time one-counter automata
We present an efficient learning algorithm for languages accepted by deterministic real time one-counter automata (ROCA). The learning algorithm works by first learning an initial segment, B n , of the infinite state machine that accepts the unknown language and then decomposing it into a complete control structure and a partial counter. A new, efficient ROCA decomposition algorithm, which will be presented in detail, allows this result. The decomposition algorithm works in time O(n2log(n)) where nc is the number of states of B n for some language-dependent constant c. If Angluin's algorithm for learning regular languages is used to learn B n and the complexity of this step is h(n, m), where m is the length of the longest counterexample necessary for Angluin's algorithm, the complexity of our algorithm is O(h(n,m) + n2log(n)).
KeywordsState Machine Finite State Machine Regular Language Exit Point Input String
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