Publication Details

Counterexample-guided inductive synthesis for probabilistic systems

ČEŠKA Milan, HENSE Christian, JUNGES Sebastian and KATOEN Joost-Pieter. Counterexample-guided inductive synthesis for probabilistic systems. Formal Aspects of Computing, vol. 33, no. 4, 2021, pp. 637-667. ISSN 0934-5043. Available from: https://dl.acm.org/doi/10.1007/s00165-021-00547-2
Czech title
Induktivní syntéza pravděpodobnostních systémů řízená protipříklady
Type
journal article
Language
english
Authors
Češka Milan, doc. RNDr., Ph.D. (DITS FIT BUT)
Hense Christian (RWTH Aachen University)
Junges Sebastian (RWTH Aachen University)
Katoen Joost-Pieter (RWTH)
URL
Keywords

Program Sketches,
Probabilistic Programming,
Markov Chains,
Model Checking,
Counterexamples

Abstract

This paper presents counterexample-guided inductive synthesis (CEGIS) to automatically synthesise probabilistic models. The starting point is a family of finite-stateMarkov chains with related but distinct topologies. Such families can succinctly be described by a sketch of a probabilistic program. Program sketches are programs containing holes. Every hole has a finite repertoire of possible program snippets by which it can be filled.We study several synthesis problems-feasibility, optimal synthesis, and complete partitioning-for a given quantitative specification . Feasibility amounts to determine a family member satisfying , optimal synthesis amounts to find a family member that maximises the probability to satisfy , and complete partitioning splits the family in satisfying and refuting members. Each of these problems can be considered under the additional constraint of minimising the total cost of instantiations, e.g., what are all possible instantiations for  that are within a certain budget? The synthesis problems are tackled using a CEGIS approach. The crux is to aggressively prune the search space by using counterexamples provided by a probabilistic model checker. Counterexamples can be viewed as sub-Markov chains that rule out all family members that share this sub-chain. Our CEGIS approach leverages efficient probabilisticmodel checking,modern SMT solving, and programsnippets as counterexamples. Experiments on case studies froma diverse nature-controller synthesis, program sketching, and security-show that synthesis among up to a million candidate designs can be done using a few thousand verification queries.

Published
2021
Pages
637-667
Journal
Formal Aspects of Computing, vol. 33, no. 4, ISSN 0934-5043
Publisher
Springer London
DOI
UT WoS
000648556100001
EID Scopus
BibTeX
@ARTICLE{FITPUB12501,
   author = "Milan \v{C}e\v{s}ka and Christian Hense and Sebastian Junges and Joost-Pieter Katoen",
   title = "Counterexample-guided inductive synthesis for probabilistic systems",
   pages = "637--667",
   journal = "Formal Aspects of Computing",
   volume = 33,
   number = 4,
   year = 2021,
   ISSN = "0934-5043",
   doi = "10.1007/s00165-021-00547-2",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/12501"
}
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