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Computer Science > Logic in Computer Science

arXiv:2009.10883v1 (cs)
[Submitted on 23 Sep 2020]

Title:LTLf Synthesis on Probabilistic Systems

Authors:Andrew M. Wells (Rice University), Morteza Lahijanian (University of Colorado at Boulder), Lydia E. Kavraki (Rice University), Moshe Y. Vardi (Rice University)
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Abstract:Many systems are naturally modeled as Markov Decision Processes (MDPs), combining probabilities and strategic actions. Given a model of a system as an MDP and some logical specification of system behavior, the goal of synthesis is to find a policy that maximizes the probability of achieving this behavior. A popular choice for defining behaviors is Linear Temporal Logic (LTL). Policy synthesis on MDPs for properties specified in LTL has been well studied. LTL, however, is defined over infinite traces, while many properties of interest are inherently finite. Linear Temporal Logic over finite traces (LTLf) has been used to express such properties, but no tools exist to solve policy synthesis for MDP behaviors given finite-trace properties. We present two algorithms for solving this synthesis problem: the first via reduction of LTLf to LTL and the second using native tools for LTLf. We compare the scalability of these two approaches for synthesis and show that the native approach offers better scalability compared to existing automaton generation tools for LTL.
Comments: In Proceedings GandALF 2020, arXiv:2009.09360
Subjects: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2009.10883 [cs.LO]
  (or arXiv:2009.10883v1 [cs.LO] for this version)
  https://doi.org/10.48550/arXiv.2009.10883
Journal reference: EPTCS 326, 2020, pp. 166-181
Related DOI: https://doi.org/10.4204/EPTCS.326.11

Submission history

From: EPTCS [view email] [via EPTCS proxy]
[v1] Wed, 23 Sep 2020 01:26:47 UTC (330 KB)
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