Learning Deterministic Multi-Clock Timed Automata from Queries and Counterexamples

Khái niệm cốt lõi

The core message of this paper is to present two active learning algorithms for deterministic timed automata (DTA) with multiple clocks. The first algorithm learns from a powerful teacher who can provide reset information, while the second algorithm learns from a normal teacher who can only answer membership and equivalence queries. The authors show that the learning problem can be reduced to learning the corresponding reset-clocked language of the target DTA, and prove the termination and correctness of the proposed algorithms.

Tóm tắt

This paper presents algorithms for actively learning deterministic timed automata (DTA) with multiple clocks. The key ideas are:

The authors define an equivalence relation over the reset-clocked language of a DTA and show that it has a finite number of equivalence classes. This allows them to reduce the problem of learning the timed language of a DTA to learning its corresponding reset-clocked language.

For the case of learning from a powerful teacher, the authors introduce reset information queries in addition to membership and equivalence queries. The learner can ask the teacher about the reset information along a run, which helps in constructing the hypothesis DTA.

For the case of learning from a normal teacher who can only answer membership and equivalence queries, the learner has to guess the reset information. This results in an exponential increase in the number of table instances the learner has to handle.

The authors prove the termination and correctness of both algorithms, and provide complexity analyses in terms of the number of queries needed.

The paper also includes a preliminary implementation of the proposed learning methods.

Thống kê

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Trích dẫn

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Thông tin chi tiết chính được chắt lọc từ

Learning Deterministic Multi-Clock Timed Automata

by Yu Teng,Miao... lúc arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07823.pdf

Yêu cầu sâu hơn

How can the proposed learning algorithms be extended to handle non-deterministic timed automata

To extend the proposed learning algorithms to handle non-deterministic timed automata, we can modify the algorithms to accommodate multiple possible transitions for a given state and input symbol. Instead of determining a single transition, the learner would need to consider all possible transitions and their corresponding reset information. The observation table would need to be expanded to capture the non-deterministic nature of the automaton, and the equivalence relation would need to account for multiple possible paths in the reset-clocked language. By adapting the algorithms to handle non-determinism, the learner can effectively learn models of non-deterministic timed automata.

What are the potential applications of the learned DTA models, and how can they be used in practice

The learned DTA models have various potential applications in practice. One application is in system verification and validation, where the learned models can be used to analyze and verify the correctness of timed systems. These models can also be utilized in real-time scheduling and planning, where the timing constraints of the system need to be considered for efficient scheduling of tasks. Additionally, the learned DTA models can be applied in fault diagnosis and system monitoring, where deviations from the expected timed behavior can be detected and analyzed. Overall, the learned DTA models provide a formal representation of timed systems that can be leveraged in a wide range of practical applications to improve system reliability and performance.

Can the learning algorithms be further optimized to reduce the number of queries required, especially in the case of learning from a normal teacher

To optimize the learning algorithms and reduce the number of queries required, especially in the case of learning from a normal teacher, several strategies can be employed. One approach is to incorporate active learning techniques that focus on selecting informative queries that provide the most relevant information for learning the model. By strategically choosing queries that maximize the learning progress, the number of queries needed can be minimized. Additionally, refining the hypothesis generation process and incorporating domain knowledge can help in making more accurate guesses about the reset information, reducing the need for additional queries. Furthermore, exploring techniques such as query synthesis and adaptive sampling can also aid in optimizing the learning process and reducing the overall query complexity.