Counterexample-Driven Synthesis for Probabilistic Program Sketches

Češka, Milan; Hensel, Christian; Junges, Sebastian; Katoen, Joost-Pieter

doi:10.1007/978-3-030-30942-8_8

Cited by 18 publications

(25 citation statements)

References 63 publications

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“…These methods are limited to small families. This motivated (1) abstractionrefinement over the MDP representation [10], and (2) counterexample-guided inductive synthesis (CEGIS) for MCs [9], mentioned earlier. The alternative problem of sketching for probabilistic programs that fit given data is studied, e.g., in [32,38].…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we recap a baseline for a counterexample-guided inductive synthesis (CEGIS) loop, as put forward in [9]. In particular, we first instantiate an oracle-guided synthesis method, discuss an operational model for families, giving structure to the parameterized set of Markov chains, and finally detail the usage of CEs to create an oracle.…”

Section: Counterexample-guided Inductive Synthesismentioning

confidence: 99%

“…Remark 1, generalize this to multiple properties and to optimal synthesis. The notion of counterexamples is at the heart of the oracle from [9] and Sect. 4.…”

Section: Counterexample-guided Oraclesmentioning

confidence: 99%

“…The oracle answers whether the family member satisfies ϕ, and crucially, gives additional information in case this is not the case. Inspired by [9], if the family member violates the specification ϕ, our oracle returns a set K of parameters such that all family members obtained by changing only the values assigned to K violate ϕ. We argue that such an oracle must (1) induce little overhead in providing K , (2) be aware of the existence of parameters in the family, and (3) have (resemblance of) awareness about the semantics of the parameters and their values.…”

Section: Introductionmentioning

confidence: 99%

“…If a hole is absent in a CE, its value is irrelevant. To avoid the cost of finding optimal CEs-an NP-hard problem [19]-we consider greedy CEs that are similar to [9]. However, our greedy CEs are aware of the parameters, and try to limit the occurrence of parameters in the CE.…”

Section: Introductionmentioning

confidence: 99%

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Inductive Synthesis for Probabilistic Programs Reaches New Horizons

Andriushchenko

Češka

Junges

et al. 2021

Tools and Algorithms for the Construction and Analysis of Systems

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This paper presents a novel method for the automated synthesis of probabilistic programs. The starting point is a program sketch representing a finite family of finite-state Markov chains with related but distinct topologies, and a reachability specification. The method builds on a novel inductive oracle that greedily generates counter-examples (CEs) for violating programs and uses them to prune the family. These CEs leverage the semantics of the family in the form of bounds on its best- and worst-case behaviour provided by a deductive oracle using an MDP abstraction. The method further monitors the performance of the synthesis and adaptively switches between inductive and deductive reasoning. Our experiments demonstrate that the novel CE construction provides a significantly faster and more effective pruning strategy leading to an accelerated synthesis process on a wide range of benchmarks. For challenging problems, such as the synthesis of decentralized partially-observable controllers, we reduce the run-time from a day to minutes.

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Section: Related Workmentioning

confidence: 99%

Section: Counterexample-guided Inductive Synthesismentioning

confidence: 99%

“…Remark 1, generalize this to multiple properties and to optimal synthesis. The notion of counterexamples is at the heart of the oracle from [9] and Sect. 4.…”

Section: Counterexample-guided Oraclesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations