Counterexample-guided inductive synthesis for probabilistic systems

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 φ . Feasibility amounts to determine a family member satisfying φ , optimal synthesis amounts to find a family member that maximises the probability to satisfy φ , 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 φ 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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 01, 2021
Source ID
10.1007/s00165-021-00547-2

Entities

People

  • Christian Hensel
  • Joost-Pieter Katoen
  • Milan Češka
  • Sebastian Junges

Organizations

  • Brno University of Technology
  • Czech Science Foundation
  • Defense Advanced Research Projects Agency
  • European Research Council
  • German Research Foundation
  • National Science Foundation
  • RWTH Aachen University
  • Toyota Motor North America
  • University of California, Berkeley

Tags

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Space