Testing Spin’s LTL Formula Conversion into Büchi Automata with Randomly Generated Input

Equivalent Characterizations of Fuzzy Büchi Automata

Chinese Journal of Computers ◽

10.3724/sp.j.1016.2013.01235 ◽

2014 ◽

Vol 36 (6) ◽

pp. 1235-1245

Author(s):

Zhao-Wei HAN ◽

Yong-Ming LI

Keyword(s):

Büchi Automata ◽

Buchi Automata

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Coinductive Algorithms for Büchi Automata

Fundamenta Informaticae ◽

10.3233/fi-2021-2046 ◽

2021 ◽

Vol 180 (4) ◽

pp. 351-373

Author(s):

Denis Kuperberg ◽

Laureline Pinault ◽

Damien Pous

Keyword(s):

State Space ◽

State Of The Art ◽

The State ◽

Specific Structure ◽

Büchi Automata ◽

Deterministic Automata ◽

Buchi Automata ◽

Language Equivalence ◽

Art Techniques

We propose a new algorithm for checking language equivalence of non-deterministic Büchi automata. We start from a construction proposed by Calbrix, Nivat and Podelski, which makes it possible to reduce the problem to that of checking equivalence of automata on finite words. Although this construction generates large and highly non-deterministic automata, we show how to exploit their specific structure and apply state-of-the art techniques based on coinduction to reduce the state-space that has to be explored. Doing so, we obtain algorithms which do not require full determinisation or complementation.

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Synthesis with Mandatory Stop Actions

10.24963/kr.2021/23 ◽

2021 ◽

Author(s):

Giuseppe De Giacomo ◽

Antonio Di Stasio ◽

Giuseppe Perelli ◽

Shufang Zhu

Keyword(s):

Model Checking ◽

Finite State Automata ◽

Deep Impact ◽

Synthesis Technique ◽

Büchi Automata ◽

Finite State ◽

Ltl Synthesis ◽

Buchi Automata ◽

The Impact

We study the impact of the need for the agent to obligatorily instruct the action stop in her strategies. More specifically we consider synthesis (i.e., planning) for LTLf goals under LTL environment specifications in the case the agent must mandatorily stop at a certain point. We show that this obligation makes it impossible to exploit the liveness part of the LTL environment specifications to achieve her goal, effectively reducing the environment specifications to their safety part only. This has a deep impact on the efficiency of solving the synthesis, which can sidestep handling Buchi determinization associated to LTL synthesis, in favor of finite-state automata manipulation as in LTLf synthesis. Next, we add to the agent goal, expressed in LTLf, a safety goal, expressed in LTL. Safety goals must hold forever, even when the agent stops, since the environment can still continue its evolution. Hence the agent, before stopping, must ensure that her safety goal will be maintained even after she stops. To do synthesis in this case, we devise an effective approach that mixes a synthesis technique based on finite-state automata (as in the case of LTLf goals) and model-checking of nondeterministic Buchi automata. In this way, again, we sidestep Buchi automata determinization, hence getting a synthesis technique that is intrinsically simpler than standard LTL synthesis.

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