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Comment on A passive automata learning tutorial with dfasat by Automata learning as a satisfiability modulo theories problem – Automaton Learning
https://automatonlearning.net/2016/11/04/a-passive-automata-learning-tutorial-with-dfasat/#comment-14
Fri, 22 Sep 2017 01:17:19 +0000https://automatonlearning.net/?p=32#comment-14[…] Deterministic finite automata (DFAs) are useful in a variety of applications. However, the problem of learning a DFA of minimal size from positive (accepted) and negative (rejected) strings can be very hard. In fact, it is the optimization variant of the problem of finding a consistent DFA of a fixed size, which has been shown to be NP-complete. In 2010, Marijn Heule and Sicco Verwer presented an algorithm that encodes the problem of learning a DFA from labeled strings as a satisfiability (SAT) problem. Their algorithm has since won the StaMinA competition, and has led to the creation of the dfasat tool (for which Chris has created an exellent tutorial). […]
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Comment on Finite State Automata for Autonomous Driving by pirroplumbi
https://automatonlearning.net/2017/03/19/finite-state-automata-for-autonomous-driving/#comment-12
Thu, 22 Jun 2017 10:04:23 +0000https://automatonlearning.net/?p=169#comment-12…thanks QIN LIN!
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Comment on Finite State Automata for Autonomous Driving by Automata learning as a satisfiability modulo theories problem – Automaton Learning
https://automatonlearning.net/2017/03/19/finite-state-automata-for-autonomous-driving/#comment-8
Mon, 24 Apr 2017 07:16:23 +0000https://automatonlearning.net/?p=169#comment-8[…] finite automata (DFAs) are useful in a variety of applications. However, the problem of learning a DFA of minimal size from positive (accepted) and negative […]
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Comment on 4 properties making automata particularly interpretable by Why is learning so effective in software testing? – Automaton Learning
https://automatonlearning.net/2016/12/07/4-properties-making-automata-interpretable/#comment-7
Wed, 01 Mar 2017 16:25:40 +0000https://automatonlearning.net/?p=107#comment-7[…] However, in the many applications we see that the regular universe is big enough. This is partly because one can choose an alphabet small enough (or abstract enough) to exhibit regular behaviour. By choosing such a alphabet we might get a smaller, more abstract view on the actual behaviour. But it’s often enough for bug finding, and additionally it gives a model for which we have very good tooling, theory and interpretability (see Chris’ post). […]
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