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<p>Active learning of timed automata is a crucial research topic. While a recent algorithm has been proposed for active learning of deterministic timed automata, it exhibits slow learning speed, particularly for automata with large sizes, posing challenges for application in complex systems. In this paper, we propose an enhanced learning algorithm, leveraging the idea of tree-based active learning. Our approach utilizes a data structure named timed classification tree, instead of an observation table, to store information acquired during the learning process. By avoiding checking the tree status and processing useless prefixes of counterexamples, it reduces the number of membership queries and equivalence queries, thereby also accelerating the practical learning speed. The experimental results demonstrate the effectiveness of our approach.</p>

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Active learning of deterministic timed automata via timed classification tree

  • 期刊论文

  • 发表日期：

    2025年9月15日

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  • Yu Teng,
  • Hanyue Chen,
  • Junri Mi,
  • Miaomiao Zhang,
  • Jie An,
  • Naijun Zhan

摘要

Active learning of timed automata is a crucial research topic. While a recent algorithm has been proposed for active learning of deterministic timed automata, it exhibits slow learning speed, particularly for automata with large sizes, posing challenges for application in complex systems. In this paper, we propose an enhanced learning algorithm, leveraging the idea of tree-based active learning. Our approach utilizes a data structure named timed classification tree, instead of an observation table, to store information acquired during the learning process. By avoiding checking the tree status and processing useless prefixes of counterexamples, it reduces the number of membership queries and equivalence queries, thereby also accelerating the practical learning speed. The experimental results demonstrate the effectiveness of our approach.