End-to-End Verification for Subgraph Solving

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  • Stephan Gocht
  • Ciaran McCreesh
  • Magnus O. Myreen
  • Nordström, Jakob
  • Andy Oertel
  • Yong Kiam Tan

Modern subgraph-finding algorithm implementations consist of thousands of lines of highly optimized code, and this complexity raises questions about their trustworthiness. Recently, some state-of-the-art subgraph solvers have been enhanced to output machine-verifiable proofs that their results are correct. While this significantly improves reliability, it is not a fully satisfactory solution, since end-users have to trust both the proof checking algorithms and the translation of the high-level graph problem into a low-level 0-1 integer linear program (ILP) used for the proofs. In this work, we present the first formally verified toolchain capable of full end-to-end verification for subgraph solving, which closes both of these trust gaps. We have built encoder frontends for various graph problems together with a 0-1 ILP (a.k.a. pseudo-Boolean) proof checker, all implemented and formally verified in the CAKEML ecosystem. This toolchain is flexible and extensible, and we use it to build verified proof checkers for both decision and optimization graph problems, namely, subgraph isomorphism, maximum clique, and maximum common (connected) induced subgraph. Our experimental evaluation shows that end-to-end formal verification is now feasible for a wide range of hard graph problems.

OriginalsprogEngelsk
TidsskriftProceedings of the AAAI Conference on Artificial Intelligence
Vol/bind38
Udgave nummer8
Sider (fra-til)8038-8047
Antal sider10
ISSN2159-5399
DOI
StatusUdgivet - 2024
Begivenhed38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada
Varighed: 20 feb. 202427 feb. 2024

Konference

Konference38th AAAI Conference on Artificial Intelligence, AAAI 2024
LandCanada
ByVancouver
Periode20/02/202427/02/2024
SponsorAssociation for the Advancement of Artificial Intelligence

Bibliografisk note

Funding Information:
Stephan Gocht and Jakob Nordstr\u00F6m were supported by the Swedish Research Council grant 2016-00782, and Jakob Nordstr\u00F6m also received funding from the Independent Research Fund Denmark grant 9040-00389B. Ciaran McCreesh was supported by a Royal Academy of Engineering research fellowship, and by the Engineering and Physical Sciences Research Council [grant number EP/X030032/1]. Magnus Myreen was supported by Swedish Research Council grant 2021-05165. Andy Oertel was supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation. Yong Kiam Tan was supported by A*STAR, Singapore. Part of this work was carried out while taking part in the Dagstuhl workshops 22411 \u201CTheory and Practice of SAT and Combinatorial Solving\u201D and 23261 \u201CSAT Encodings and Beyond\u201D, as well as in the extended reunion of the program \u201CSatisfiability: Theory, Practice, and Beyond\u201D in the spring of 2023 at the Simons Institute for the Theory of Computing at UC Berkeley.

Publisher Copyright:
Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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