The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Strips: A new approach to the application of theorem proving to problem solving
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In the citing paper
“Related to program synthesis are the fields of automated theorem proving (Loveland, 2016) and automated planning (Ghallab et al., 2004).Both areas study methods for efficient automated reasoning over formal models, with the former focused on reasoning over mathematical theories and program logics, and with the latter, by analogy (Fikes & Nilsson, 1971), focused on reasoning over theories of”
From Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems (2024) · cites this paper as Fikes & Nilsson, 1971
In the citing paper
“The factored form subsumes STRIPS operators, where the add and delete lists are the difference between an object before and after, with conditional effects organized by type rather than enumerated [Fikes and Nilsson, 1971, Pednault, 1989, McDermott et al., 1998].”
From OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration for ARC-AGI-3 (2026) · cites this paper as Fikes and Nilsson, 1971
In the citing paper
“Regression planning presupposes a goal written as a conjunction of facts with operators that declare which facts they add and delete, the STRIPS form [Fikes and Nilsson, 1971].”
From OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration for ARC-AGI-3 (2026) · cites this paper as Fikes and Nilsson, 1971
Citations
Cites
(1997) · Naming the Mind: How Psychology Found its Language · doi:10.4135/9781446221815.n7 · Cited by 15,148
Some Philosophical Problems from the Standpoint of Artificial Intelligence
McCarthy, J., & Hayes, P. J. (1981) · Elsevier eBooks · doi:10.1016/b978-0-934613-03-3.50033-7 · Cited by 3,296
Problem-Solving Methods in Artificial Intelligence
Nilsson, N. J. (1971) · CERN Document Server (European Organization for Nuclear Research) · Cited by 1,607
Self-citationGPS : a case study in generality and problem solving
Ernst, G. W., & Newell, A. (1969) · Cited by 503
Green, C. (1981) · Elsevier eBooks · doi:10.1016/b978-0-934613-03-3.50019-2 · Cited by 459
Application of Theorem Proving to Problem Solving
Green, C. (1969) · doi:10.21236/ada459656 · Cited by 290
Toward automatic program synthesis
Manna, Z., & Waldinger, R. J. (1971) · Communications of the ACM · doi:10.1145/362566.362568 · Cited by 285
PROW: a step toward automatic program writing
Waldinger, R. J., & Lee, R. C. T. (1969) · International Joint Conference on Artificial Intelligence · Cited by 114
Extracting information from resolution proof trees
Luckham, D. C., & Nilsson, N. J. (1971) · Artificial Intelligence · doi:10.1016/0004-3702(71)90003-8 · Cited by 61
Self-citationMonitored Execution of Robot Plans Produced by Strips
Fikes, R. E. (1971) · doi:10.21236/ada459662 · Cited by 57
Self-citation
Research works printed but not linked: 1
- Garvey, 1969, User's guide to QA3.5 Question-Answering System, Stanford Research Institute Artifical Intelligence Group Technical Note 15
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Anderson, J. R., Farrell, R., & Sauers, R. (1984) · Cognitive Science · doi:10.1207/s15516709cog0802_1 · Cited by 422
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Gordon, T. F. (1994) · Artificial Intelligence and Law · doi:10.1007/bf00871972 · Cited by 222
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Krüger, N., Geib, C., Piater, J., Petrick, R. P. A., Steedman, M. J., Wörgötter, F., Ude, A., Asfour, T., Kraft, D., Omrčen, D., et al. (2011) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2011.05.009 · Cited by 155
Metadata
- Authors
- Fikes, R. E., & Nilsson, N. J.
- Affiliations
- SRI International (US)
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 1971-12
- Volume
- 2
- Issue
- 3-4
- Pages
- 189-208
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2337392266
- Cited by
- 4,882
- References
- 13
- Citations by year
- 2026: 53 · 2025: 86 · 2024: 71 · 2023: 94 · 2022: 132 · 2021: 135 · 2020: 138 · 2019: 112 · 2018: 169 · 2017: 133 · 2016: 114 · 2015: 128 · 2014: 165 · 2013: 159 · 2012: 157
- FWCI
- 16.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Artificial Intelligence (publishedVersion)
- American Association for Artificial Intelligence eBooks
- International Joint Conference on Artificial Intelligence
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › Constraint Satisfaction and Optimization
- Keywords
- STRIPS, problem solving, theorem proving, means-ends analysis, world models, first-order logic, operator sequences, goal satisfaction
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