Counterexample-guided inductive synthesis for probabilistic systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU140799" target="_blank" >RIV/00216305:26230/21:PU140799 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/doi/10.1007/s00165-021-00547-2" target="_blank" >https://dl.acm.org/doi/10.1007/s00165-021-00547-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00165-021-00547-2" target="_blank" >10.1007/s00165-021-00547-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Counterexample-guided inductive synthesis for probabilistic systems

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Counterexample-guided inductive synthesis for probabilistic systems

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GJ20-02328Y" target="_blank" >GJ20-02328Y: CAQtuS: Počítačem podporovaná kvantitativní syntéza</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Formal Aspects of Computing

ISSN

0934-5043

e-ISSN

1433-299X

Svazek periodika

33

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

30

Strana od-do

637-667

Kód UT WoS článku

000648556100001

EID výsledku v databázi Scopus

2-s2.0-85105498146