Cognitive architectures: Research issues and challenges
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Investment in and application of autonomy has yielded significant breakthroughs in areas such as Power Systems, Mission & Flight Operations, and On-Orbit Assembly & Docking.
“Over the years, the investment in and application of autonomy has yielded significant breakthroughs in areas such as: Power Systems, Mission & Flight Operations, On-Orbit Assembly & Docking [116].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [116]
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works
A robust layered control system for a mobile robot
Brooks, R. (1986) · IEEE Journal on Robotics and Automation · doi:10.1109/jra.1986.1087032 · Cited by 7,799 · cited 1× · see citing works
“Some architectures (e.g., Carbonell et al., 1990) lean toward deliberation because they grew out of research on problem solving and planning, whereas other frameworks (e.g., Brooks, 1986) emphasize reactive execution to the exclusion of deliberation.”
BackgroundCase-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches
Aamodt, A., & Plaza, E. (1994) · AI Communications · doi:10.3233/aic-1994-7104 · Cited by 5,624 · see citing works
YOU CAN'T PLAY 20 QUESTIONS WITH NATURE AND WIN: PROJECTIVE COMMENTS ON THE PAPERS OF THIS SYMPOSIUM
Newell, A. (1973) · Visual Information Processing · doi:10.1016/b978-0-12-170150-5.50012-3 · Cited by 820 · cited 3× · see citing works
“Newell (1973a) has argued persuasively for systems-level research in cognitive science and artificial intelligence, claiming ‘‘You can’t play 20 questions with nature and win ” .”
BackgroundMethod
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Lieder, F., & Griffiths, T. L. (2020) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1900061x · Cited by 892 · see citing works
Laird, J. E., Lebiere, C., & Rosenbloom, P. S. (2017) · AI Magazine · doi:10.1609/aimag.v38i4.2744 · Cited by 392 · see citing works
“Typical research efforts on cognitive architectures (Langley, Laird, and Rogers 2009) are concerned with much more than just the architectural level — and thus may be more appropriately thought of as developing more comprehensive cognitive systems — although none has yet spanned the entire…”
BackgroundSelf-citationA review of large language models and autonomous agents in chemistry
Ramos, M. C., Collison, C. J., & White, A. D. (2025) · Chemical Science · doi:10.1039/d4sc03921a · Cited by 274 · see citing works
Decision Making in Multiagent Systems: A Survey
Rizk, Y., Awad, M., & Tunstel, E. W. (2018) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2018.2840971 · Cited by 219 · see citing works
The timing of the cognitive cycle
Madl, T., Baars, B. J., & Franklin, S. (2011) · PLoS ONE · doi:10.1371/journal.pone.0014803 · Cited by 116 · see citing works
The brain's connective core and its role in animal cognition
Shanahan, M. (2012) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2012.0128 · Cited by 115 · see citing works
A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy
Baum, S. D. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3070741 · Cited by 93 · see citing works
Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68 · see citing works
“An intelligent agent capable of autonomy must exhibit some fundamental properties such as reasoning, learning, and problem-solving [115] .”
BackgroundMunawar, S., Toor, S. K., Aslam, M., & Hamid, M. (2018) · EURASIA Journal of Mathematics, Science and Technology Education · doi:10.29333/ejmste/85036 · Cited by 54 · see citing works
A cognitive architecture theory of anticipatory thinking
Jones, S. J., & Laird, J. E. (2023) · AI Magazine · doi:10.1002/aaai.12102 · Cited by 5 · see citing works
Self-citationWhen Can AI Models Explain Learning? Validity Criteria for AI as Cognitive Models in Education
Wang, P., Viberg, O., Law, E. L.-C., & Greiff, S. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10202-y · Cited by 3 · see citing works
- Authors
- Langley, P., Laird, J. E., & Rogers, S.
- Affiliations
- University of Michigan (US)
- Stanford University (US)
- Venue
- Cognitive Systems Research
- Publisher
- Elsevier BV
- Published
- 2009-06
- Volume
- 10
- Issue
- 2
- Pages
- 141-160
- ISSN
- 1389-0417 (ISSN-L)
- OpenAlex
- W2117553866
- Cited by
- 728
- References
- 4
- Citations by year
- 2026: 31 · 2025: 28 · 2024: 21 · 2023: 44 · 2022: 27 · 2021: 34 · 2020: 37 · 2019: 40 · 2018: 51 · 2017: 45 · 2016: 54 · 2015: 53 · 2014: 38 · 2013: 48 · 2012: 64
- FWCI
- 52.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cognitive Systems Research (publishedVersion)
- ResearchSpace (University of Auckland) (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
- cognitive architecture, representation, organizations, learning, performance, open problems
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 ›
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