Efficient Symbolic Reactive Synthesis for Finite-Horizon Tasks

He, Keliang; Wells, Andrew M.; Kavraki, Lydia E.; Vardi, Moshe Y.

doi:10.1109/icra.2019.8794170

Cited by 38 publications

(43 citation statements)

References 14 publications

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“…Our work opens the door to the use of LTL f synthesis on practical domains such as robotics, cf. [14]. Our tool is on GitHub [25].…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, as the first set of test formulas for the Gridworld MDP, we considered a natural finite-trace specification where the agent has n goals to accomplish in any order, as well as a "safety" property where it must globally avoid some states. This is typical of e.g., a robotics domain [14]. We use Fn to denote this formula for a given n. For the second set of test formulas, we keep regions to visit and avoid but introduce some ordering and repeat visits.…”

Section: Test Formulasmentioning

confidence: 99%

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LTLf Synthesis on Probabilistic Systems

Wells

Lahijanian

Kavraki

et al. 2020

Electron. Proc. Theor. Comput. Sci.

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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 (LTL f) 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 LTL f to LTL and the second using native tools for LTL f. 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.

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“…Our work opens the door to the use of LTL f synthesis on practical domains such as robotics, cf. [14]. Our tool is on GitHub [25].…”

Section: Resultsmentioning

confidence: 99%

Section: Test Formulasmentioning

confidence: 99%

LTLf Synthesis on Probabilistic Systems

Wells

Lahijanian

Kavraki

et al. 2020

Electron. Proc. Theor. Comput. Sci.

Self Cite

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show abstract

“…Both (Tabajara and Vardi 2019) and (Camacho et al 2018) benefit from compositional techniques as they both decompose the input formula into conjuncts before construction of the respective automata. Application-specific decomposition has also been shown to lead to an orders-of-magnitude improvement in LTLf synthesis for robotics (He et al 2019).…”

Section: Compositional Techniques In Temporal Synthesismentioning

confidence: 99%

Hybrid Compositional Reasoning for Reactive Synthesis from Finite-Horizon Specifications

Bansal

Tabajara

et al. 2020

AAAI

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LTLf synthesis is the automated construction of a reactive system from a high-level description, expressed in LTLf, of its finite-horizon behavior. So far, the conversion of LTLf formulas to deterministic finite-state automata (DFAs) has been identified as the primary bottleneck to the scalabity of synthesis. Recent investigations have also shown that the size of the DFA state space plays a critical role in synthesis as well.Therefore, effective resolution of the bottleneck for synthesis requires the conversion to be time and memory performant, and prevent state-space explosion. Current conversion approaches, however, which are based either on explicit-state representation or symbolic-state representation, fail to address these necessities adequately at scale: Explicit-state approaches generate minimal DFA but are slow due to expensive DFA minimization. Symbolic-state representations can be succinct, but due to the lack of DFA minimization they generate such large state spaces that even their symbolic representations cannot compensate for the blow-up.This work proposes a hybrid representation approach for the conversion. Our approach utilizes both explicit and symbolic representations of the state-space, and effectively leverages their complementary strengths. In doing so, we offer an LTLf to DFA conversion technique that addresses all three necessities, hence resolving the bottleneck. A comprehensive empirical evaluation on conversion and synthesis benchmarks supports the merits of our hybrid approach.

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“…While how to translate PDDL into DFA is well-known (De Giacomo and Rubin 2018), how to perform it in a practically efficient and optimized way is open to further investigation. A promising direction for the translation might be borrowing insights from reactive planning (He et al 2019). Instead of having only agent actions with nondeterministic effects, the reactive planning domain has agent actions and environment actions.…”

Section: Experimental Analysismentioning

confidence: 99%

Two-Stage Technique for LTLf Synthesis Under LTL Assumptions

Giacomo

Stasio

Vardi

et al. 2020

Proceedings of the Seventeenth International Conference on Principles of Knowledge Representation and Reasoning

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In synthesis, assumption are constraints on the environments that rule out certain environment behaviors. A key observation is that even if we consider system with LTLf goals on finite traces, assumptions need to be expressed considering infinite traces, using LTL on infinite traces, since the decision to stop the trace is controlled by the agent. To solve synthesis of LTLf goals under LTL assumptions, we could reduce the problem to LTL synthesis. Unfortunately, while synthesis in LTLf and in LTL have the same worst-case complexity (both are 2EXPTIME-complete), the algorithms available for LTL synthesis are much harder in practice than those for LTLf synthesis. Recently, it has been shown that in basic forms of fairness and stability assumptions we can avoid such a detour to LTL and keep the simplicity of LTLf synthesis. In this paper, we generalize these results and show how to effectively handle any kind of LTL assumptions. Specifically, we devise a two-stage technique for solving LTLf under general LTL assumptions and show empirically that this technique performs much better than standard LTL synthesis.

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Efficient Symbolic Reactive Synthesis for Finite-Horizon Tasks

Cited by 38 publications

References 14 publications

LTLf Synthesis on Probabilistic Systems

LTLf Synthesis on Probabilistic Systems

Hybrid Compositional Reasoning for Reactive Synthesis from Finite-Horizon Specifications

Two-Stage Technique for LTLf Synthesis Under LTL Assumptions

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