The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Procedural knowledge
Abstract
Much of commonsense knowledge about the real world is in the form of procedures or sequences of actions for achieving particular goals. In this paper, a formalism is presented for representing such knowledge using the notion of process. A declarative semantics for the representation is given, which allows a user to state facts about the effects of doing things in the problem domain of interest. An operational semantics is also provided, which shows how this knowledge can be used to achieve particular goals or to form intentions regarding their achievement. Given both semantics, our formalism additionally serves as an executable specification language suitable for constructing complex systems. A system based on this formalism is described, and examples involving control of an autonomous robot and fault diagnosis for NASA's space shuttle are provided.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Maintaining knowledge about temporal intervals
Allen, J. F. (1983) · Communications of the ACM · doi:10.1145/182.358434 · Cited by 7,593
STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving
Fikes, R. E., & Nilsson, N. J. (1971) · Artificial Intelligence · doi:10.1016/0004-3702(71)90010-5 · Cited by 4,882
Cited by
Alternative Approaches to Second Language Acquisition
(2011) · Routledge eBooks · doi:10.4324/9780203830932 · Cited by 1,079
Remote Agent: to boldly go where no AI system has gone before
Muscettola, N., Nayak, P. P., Pell, B., & Williams, B. C. (1998) · Artificial Intelligence · doi:10.1016/s0004-3702(98)00068-x · Cited by 698
40 years of cognitive architectures: core cognitive abilities and practical applications
Kotseruba, I., & Tsotsos, J. K. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-018-9646-y · Cited by 539
A methodology and modelling technique for systems of BDI agents
Kinny, D., Georgeff, M., & Rao, A. (1996) · Lecture Notes in Computer Science · doi:10.1007/bfb0031846 · Cited by 358
Self-citationA review of agent-based programming for multi-agent systems
Cardoso, R. C., & Ferrando, A. (2021) · Computers · doi:10.3390/computers10020016 · Cited by 133
Metadata
- Authors
- Georgeff, M. P., & Lansky, A. L.
- Venue
- Proceedings of the IEEE
- Published
- 1986
- Volume
- 74
- Issue
- 10
- Pages
- 1383-1398
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2296400517
- Cited by
- 329
- References
- 2
- FWCI
- 5.19
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Procedural knowledge. https://app.cochat.ai/papers/10.1109/proc.1986.13639
- The paper
- Georgeff, M. P., & Lansky, A. L. (1986). Procedural knowledge. Proceedings of the IEEE. https://doi.org/10.1109/proc.1986.13639
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.