SOAR: An Architecture for General Intelligence
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Production systems were incorporated into cognitive architectures that specified control flow for selecting, applying, and even generating new productions.
“To do so, they were incorporated into cognitive architectures that specified control flow for selecting, applying, and even generating new productions (Laird et al., 1987; Laird, 2022; Kotseruba and Tsotsos, 2020).”
From Cognitive Architectures for Language Agents (2023) · cites this paper as Laird et al., 1987
Cites
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
Miller, G. A. (1956) · Psychological Review · doi:10.1037/h0043158 · Cited by 17,812 · see citing works
Chase, W. G., & Simon, H. A. (1973) · Cognitive Psychology · doi:10.1016/0010-0285(73)90004-2 · Cited by 4,479 · cited 1× · see citing works
“It has been much studied in psychology [7, 12, 43, 50] and it was developed into an explicit learning mechanism within a production-system architecture in prior work [35, 61, 63].”
Principles of Artificial Intelligence
Nilsson, N. J. (1982) · doi:10.1007/978-3-662-09438-9 · Cited by 3,457 · cited 1× · see citing works
“In AI, problem spaces are commonly used for genuine problem solving [18, 51, 57, 58, 59, 72], but procedural representations are commonly used for routine behavior.”
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Cognitive Load During Problem Solving: Effects on Learning
Sweller, J. (1988) · Cognitive Science · doi:10.1016/0364-0213(88)90023-7 · Cited by 11,048 · see citing works
Conflict monitoring and cognitive control
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001) · Psychological Review · doi:10.1037/0033-295x.108.3.624 · Cited by 7,351 · see citing works
Toward an architecture for never-ending language learning
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E., & Mitchell, T. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7519 · Cited by 2,000 · see citing works
Organizational routines are stored as procedural memory: Evidence from a laboratory study
Cohen, M. D., & Bacdayan, P. (1994) · Organization Science · doi:10.1287/orsc.5.4.554 · Cited by 1,393 · see citing works
The cognitive atlas: toward a knowledge foundation for cognitive neuroscience
Poldrack, R. A., Kittur, A., Kalar, D., Miller, E., Seppa, C., Gil, Y., Parker, D. S., Sabb, F. W., & Bilder, R. M. (2011) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00017 · Cited by 385 · see citing works
Generality in artificial intelligence
McCarthy, J. (1987) · Communications of the ACM · doi:10.1145/33447.33448 · Cited by 391 · see citing works
The nature and transfer of cognitive skills
Taatgen, N. A. (2013) · Psychological Review · doi:10.1037/a0033138 · Cited by 288 · see citing works
It knows what you're going to do: adding anticipation to a Quakebot
Laird, J. E. (2001) · Proceedings of the fifth international conference on Autonomous agents · doi:10.1145/375735.376343 · Cited by 246 · see citing works
Self-citationPredicting the effects of in-car interface use on driver performance: an integrated model approach
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The timing of the cognitive cycle
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Agentic AI: A Comprehensive Survey of Technologies, Applications, and Societal Implications
Pati, A. K. (2025) · IEEE Access · doi:10.1109/access.2025.3585609 · Cited by 100 · see citing works
Cognitive Architectures for Language Agents
Sumers, T. R., Yao, S., Narasimhan, K., & Griffiths, T. L. (2023) · arXiv · doi:10.48550/arxiv.2309.02427 · Cited by 72 · see citing works
“To do so, they were incorporated into cognitive architectures that specified control flow for selecting, applying, and even generating new productions (Laird et al., 1987; Laird, 2022; Figure 1: Different uses of large language models (LLMs).”
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When Can AI Models Explain Learning? Validity Criteria for AI as Cognitive Models in Education
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SmartPlay: A Benchmark for LLMs as Intelligent Agents
Wu, Y., Tang, X., Mitchell, T. M., & Li, Y. (2023) · arXiv · doi:10.48550/arxiv.2310.01557 · Cited by 6 · see citing works
“…agents (Wooldridge & Jennings, 1995), that perceives its environment and perform autonomous actions , has been one of the core objectives of A.I. (Laird et al., 1987; Russell, 2010) Recently, large language models (LLMs) (Smith et al., 2022; Chowdhery et al., 2022; Ope-nAI, 2023; Manyika; Driess…”
BackgroundInfluential
- Authors
- Laird, J. E., Newell, A., & Rosenbloom, P. S.
- Affiliations
- Palo Alto Research Center (US)
- Carnegie Mellon University (US)
- Stanford University (US)
- ORCID
- Laird, John E.
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 1987-09
- Volume
- 33
- Issue
- 1
- Pages
- 1-64
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2180809782
- Cited by
- 2,535
- References
- 3
- Citations by year
- 2026: 72 · 2025: 74 · 2024: 31 · 2023: 51 · 2022: 45 · 2021: 36 · 2020: 42 · 2019: 58 · 2018: 68 · 2017: 52 · 2016: 56 · 2015: 57 · 2014: 82 · 2013: 99 · 2012: 89
- FWCI
- 59.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Artificial Intelligence (publishedVersion)
- Defense Technical Information Center (DTIC) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Computing and Networks
- Keywords
- SOAR, cognitive architecture, general intelligence, general problem solving, cognitive modeling, learning, problem-solving strategies, knowledge representation
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