Expert system verification and validation: a survey and tutorial
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The formal verification of AI-enabled systems literature builds on more classic work on formal verification of control policies and classical expert systems, to develop methods for formal verification of neural networks and other machine learning systems.
“This builds on more classic work on formal verification of control policies (e.g., (Tabuada, 2009)) and classical expert systems (O'Keefe & O'Leary, 1993), to develop methods for formal verification of neural networks and other machine learning systems.”
From Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems (2024) · cites this paper as O'Keefe & O'Leary, 1993
Cites
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290 · cited 1× · see citing works
“In the classic Turing test (Turing 1950), a third-party has access to the output from both machine and human, and has to determine which is which.”
Background
Flagged first, then by citations
Cited by
Sorted by citations
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems
Dalrymple, D. "., Skalse, J., Bengio, Y., Russell, S., Tegmark, M., Seshia, S., Omohundro, S., Szegedy, C., Goldhaber, B., Ammann, N., et al. (2024) · arXiv · doi:10.48550/arxiv.2405.06624 · Cited by 12 · see citing works
- Authors
- O'Keefe, R. M., & O'Leary, D. E.
- Venue
- Artificial Intelligence Review
- Published
- 1993-02
- Volume
- 7
- Issue
- 1
- Pages
- 3-42
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W1981311312
- Cited by
- 205
- References
- 1
- Citations by year
- 2026: 9 · 2025: 7 · 2024: 3 · 2023: 3 · 2022: 9 · 2021: 7 · 2020: 2 · 2019: 3 · 2018: 13 · 2017: 8 · 2016: 3 · 2015: 8 · 2014: 5 · 2013: 3 · 2012: 8
- FWCI
- 16.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Artificial Intelligence Review (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- expert systems, verification, validation, credibility, anomaly detection, consistency, completeness, correctness, redundancy, automated verification tools, validation criteria, objectivity, reliability, development life cycle, model development
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
