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Optimal Translation of LTL to Limit Deterministic Automata

  • Dileep KiniEmail author
  • Mahesh Viswanathan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10206)

Abstract

A crucial step in model checking Markov Decision Processes (MDP) is to translate the \(\mathrm {LTL}\) 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 \(\mathrm {LTL}{}{\setminus }{G}{U}\) 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 \(\mathrm {LTL}{}{\setminus }{G}{U}\) but for a larger fragment \(\mathrm {LTL}_\mathrm{D}\) 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.

Keywords

Model Check Markov Decision Process Linear Temporal Logic Input Symbol Model Check Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Illinois at Urbana-ChampaignUrbanaUSA

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