Learning Büchi Automata and Its Applications

Li, Yong; Turrini, Andrea; Chen, Yu‐Fang; Zhang, Lijun

doi:10.1007/978-3-030-17601-3_2

Cited by 3 publications

(2 citation statements)

References 91 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, their method suffers from vanishing gradients and does not scale well with the automaton's complexity. An in-depth review of active automata learning is provided in [9], [10]. However, this body of work assumes a ground truth automaton ready for query and learns in a discrete state space (in terms of atomic propositions).…”

Section: Related Workmentioning

confidence: 99%

Learning an Explainable Trajectory Generator Using the Automaton Generative Network (AGN)

Rosman²,

Gilitschenski

et al. 2022

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

Symbolic reasoning is a key component for enabling practical use of data-driven planners in autonomous driving. In that context, deterministic finite state automata (DFA) are often used to formalize the underlying high-level decision-making process. Manual design of an effective DFA can be tedious. In combination with deep learning pipelines, DFA can serve as an effective representation to learn and process complex behavioral patterns. The goal of this work is to leverage that potential. We propose the automaton generative network (AGN), a differentiable representation of DFAs. The resulting neural network module can be used standalone or as an embedded component within a larger architecture. In evaluations on deep learning based autonomous vehicle planning tasks, we demonstrate that incorporating AGN improves the explainability, sample efficiency, and generalizability of the model.Index terms -learning automata, robot learning, autonomous systems,

show abstract

Section: Related Workmentioning

confidence: 99%

Learning an Explainable Trajectory Generator Using the Automaton Generative Network (AGN)

Rosman²,

Gilitschenski

et al. 2022

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

show abstract

“…[23] developed a tool supporting the transformation of sequence diagrams to Event B [24]. [25] showed the use of multi layer transformations to generate Büchi automata [26] from UML 2.0 SD. [27] defined a method for translating UML sequence diagrams annotated with MARTE stereotypes to time Petri nets with action duration specifications.…”

Section: Related Workmentioning

confidence: 99%

A Graph Transformation Approach for Modeling and Verification of UML 2.0 Sequence Diagrams

Hamrouche

Chaoui

Mazouzi

2022

cai

View full text Add to dashboard Cite

Unified Modeling Language (UML) 2.0 Sequence Diagrams (UML 2.0 SD) are used to describe interactions in software systems. These diagrams must be verified in the early stages of software development process to guarantee the production of a reliable system. However, UML 2.0 SD lack formal semantics as all UML specifications, which makes their verification difficult, especially if we are modeling a critical system where the automation of verification is necessary. Communicating Sequential Processes (CSP) is a formal specification language that is

show abstract

On Implementation of the Improved Assume-Guarantee Verification Method for Timed Systems

Tran

Nguyen

Hùng

2019

Proceedings of the Tenth International Symposium on Information and Communication Technology - SoICT 2019

View full text Add to dashboard Cite

Learning Büchi Automata and Its Applications

Cited by 3 publications

References 91 publications

Learning an Explainable Trajectory Generator Using the Automaton Generative Network (AGN)

Learning an Explainable Trajectory Generator Using the Automaton Generative Network (AGN)

A Graph Transformation Approach for Modeling and Verification of UML 2.0 Sequence Diagrams

On Implementation of the Improved Assume-Guarantee Verification Method for Timed Systems

Contact Info

Product

Resources

About