The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Sequential abstract-state machines capture sequential algorithms
Abstract
We examine sequential algorithms and formulate a sequential-time postulate, an abstract-state postulate, and a bounded-exploration postulate . Analysis of the postulates leads us to the notion of sequential abstract-state machine and to the theorem in the title. First we treat sequential algorithms that are deterministic and noninteractive. Then we consider sequential algorithms that may be nondeterministic and that may interact with their environments.
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Citations
Cites
The Art of Computer Programming
Knuth, D. E. (1968) · Cited by 16,000
On computable numbers, with an application to the entscheidungsproblem
Turing, A. M. (1937) · Proceedings of the London Mathematical Society · doi:10.1112/plms/s2-42.1.230 · Cited by 8,283
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An Unsolvable Problem of Elementary Number Theory
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Parallel program design: a foundation
Jesshope, C. (1989) · Microprocessors and Microsystems · doi:10.1016/0141-9331(89)90152-x · Cited by 841
On Computable Numbers, with an Application to the Entscheidungsproblem (1936)
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Church's Thesis and Principles for Mechanisms
(1980) · Studies in Logic and the Foundations of Mathematics · doi:10.1016/s0049-237x(08)71257-6 · Cited by 331
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Savage, J. E. (1998) · Cited by 293
Cited by
The Nature of Physical Computation
Shagrir, O. (2022) · doi:10.1093/oso/9780197552384.001.0001 · Cited by 70
Metadata
- Authors
- Gurevich, Y.
- Venue
- ACM Transactions on Computational Logic
- Published
- 2000-07
- Volume
- 1
- Issue
- 1
- Pages
- 77-111
- ISSN
- 1529-3785 (ISSN-L)
- 1557-945X
- OpenAlex
- W1998333922
- Cited by
- 458
- References
- 14
- FWCI
- 18.74
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
