Limit-Deterministic Büchi Automata for Linear Temporal Logic

Sickert, Salomon; Esparza, Javier; Jaax, Stefan; Křetínský, Jan

doi:10.1007/978-3-319-41540-6_17

Cited by 76 publications

(149 citation statements)

References 25 publications

Supporting

Mentioning

144

Contrasting

Order By: Relevance

“…MDP model checkers like PRISM [16], for a long time have used the translation from LTL to deterministic Rabin automata and only recently [21] have started using limit deterministic Büchi automata. As a consequence we compare the performance of our method against Rabinizer 3 [13] (the best known tool for translating LTL to deterministic automata) and ltl2ldba [21] (the only other known tool for translating LTL to LDBA).…”

Section: Resultsmentioning

confidence: 99%

“…As a consequence we compare the performance of our method against Rabinizer 3 [13] (the best known tool for translating LTL to deterministic automata) and ltl2ldba [21] (the only other known tool for translating LTL to LDBA). Rabinizer 3 constructs deterministic Rabin automata with generalized Rabin pairs (DGRA).…”

Section: Resultsmentioning

confidence: 99%

“…In this paper we continue the line of work started in [9,21], and present a new translation of the full LTL logic to limit deterministic Büchi automata. The new translation can be shown to be a generalization of the construction in [9] in that it constructs exponential sized automata for LTL\GU .…”

Section: Introductionmentioning

confidence: 93%

“…Recently, a translation from full LTL logic to limit deterministic automata has been proposed [21]. This translation is very similar to the translation to deterministic automata proposed in [5], with the use of nondeterminism being limited to simplifying the acceptance condition.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, like the deterministic translations of LTL, it can be shown to construct doubly exponential sized automata even for very simple LTL fragments like those that contain ∨, ∧, and F. Thus, it does not achieve the optimal bounds for LTL\GU shown in [9]. However, one advantage of the construction in [21] is that it can be used in quantitative verification as well as qualitative verification of MDPs and has been implemented in [22]. Quantitative verification of MDPs can also be performed using nondeterministic automata that have the good-for-games (GFG) property [8,12], but translating a general NBA into a GFG automaton is known to result in an exponential blow-up.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimal Translation of LTL to Limit Deterministic Automata

Kini

Viswanathan

2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. A crucial step in model checking Markov Decision Processes (MDP) is to translate the LTL specification into automata. Efforts have been made in improving deterministic automata construction for LTL but such translations are double exponential in the worst case. For model checking MDPs though limit deterministic automata suffice. Recently it was shown how to translate the fragment LTL\GU to exponential sized limit deterministic automata which speeds up the model checking problem by an exponential factor for that fragment. In this paper we show how to construct limit deterministic automata for full LTL. This translation is not only efficient for LTL\GU but for a larger fragment LTLD which is provably more expressive. We show experimental results demonstrating that our construction yields smaller automata when compared to state of the art techniques that translate LTL to deterministic and limit deterministic automata.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal Translation of LTL to Limit Deterministic Automata

Kini

Viswanathan

2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

Owl: A Library for $$\omega $$-Words, Automata, and LTL

Křetínský

Meggendorfer

Sickert

2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Omega-Regular Objectives in Model-Free Reinforcement Learning

Hahn

Perez

Schewe

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

We provide the first solution for model-free reinforcement learning of ω-regular objectives for Markov decision processes (MDPs). We present a constructive reduction from the almost-sure satisfaction of ω-regular objectives to an almostsure reachability problem, and extend this technique to learning how to control an unknown model so that the chance of satisfying the objective is maximized. A key feature of our technique is the compilation of ω-regular properties into limitdeterministic Büchi automata instead of the traditional Rabin automata; this choice sidesteps difficulties that have marred previous proposals. Our approach allows us to apply model-free, off-the-shelf reinforcement learning algorithms to compute optimal strategies from the observations of the MDP. We present an experimental evaluation of our technique on benchmark learning problems.An ω-word w on an alphabet Σ is a function w : N → Σ. We abbreviate w(i) by w i . The set of ω-words on Σ is written Σ ω and a subset of Σ ω is an ω-language on Σ.A probability distribution over a finite set S is a function d : S→[0, 1] such that s∈S d(s) = 1. Let D(S) denote the set of all discrete distributions over S. We say a distribution d ∈ D(S) is a point distribution if d(s)=1 for some s ∈ S. For a distribution d ∈ D(S) we write supp(d) def = {s ∈ S : d(s) > 0}.

show abstract

Limit-Deterministic Büchi Automata for Linear Temporal Logic

Cited by 76 publications

References 25 publications

Optimal Translation of LTL to Limit Deterministic Automata

Optimal Translation of LTL to Limit Deterministic Automata

Owl: A Library for $$\omega $$-Words, Automata, and LTL

Omega-Regular Objectives in Model-Free Reinforcement Learning

Contact Info

Product

Resources

About