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Cited by 301Reinforcement Learning in RoboticsAI-based Problem Solving and PlanningMachine Learning and Algorithms

The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.

Learning to perceive and act by trial and error

Open access
Whitehead, Steven D., Ballard, Dana H.·1991·Machine Learning
DOI 10.1007/bf00058926
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64 of 64 references verified0 citation errors · 0 not checkable

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Citations

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Research works printed but not linked: 5

  1. Anderson, C. W. (1989). Towers of hanoi with connectionist networks: Learning new features. Proceedings of the Sixth International Conference on Machine Learning (pp. 345?350). San Mateo, CA: Morgan Kaufmann.
  2. Bellman, R. E. (1957). Dynamic programming. Princeton, NJ: Princeton University Press.
  3. Nilsson, N. J. (1989). Action networks. Proceedings of the Rochester Planning Workshop (Technical Report 284) (pp. 36?68). University of Rochester, Department of Computer Science.
  4. Whitehead, S. D., & Ballard, D. H. (1991). A study of cooperative mechanisms for faster reinforcement learning (Technical Report TR 365). Rochester, NY: University of Rochester, Department of Computer Science.
  5. Williams, R. J. (1987). Reinforcement-learning connectionist systems (Technical Report NU-CCS-87?3). Boston, MA: Northeastern University, College of Computer Science.

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Metadata

Authors
Whitehead, S. D., & Ballard, D. H.
Affiliations
University of Rochester (US)
Venue
Machine Learning
Publisher
Springer Science and Business Media LLC
Published
1991-07
Volume
7
Issue
1
Pages
45-83
ISSN
  • 0885-6125 (ISSN-L)
  • 1573-0565
DOI
10.1007/bf00058926
OpenAlex
W2052117683
Cited by
300
References
64
Citations by year
2026: 6 · 2025: 4 · 2024: 6 · 2023: 6 · 2022: 3 · 2021: 14 · 2020: 4 · 2019: 7 · 2018: 7 · 2017: 11 · 2016: 6 · 2015: 3 · 2014: 4 · 2013: 10 · 2012: 7
FWCI
12.15
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
Machine Learning (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Keywords
active perception, perceptual aliasing, reinforcement learning, adaptive control architecture, sensorimotor system, visual attention, decision-making systems, internal representations

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CoChat. (2026). Citation check: Learning to perceive and act by trial and error. https://app.cochat.ai/papers/10.1007/bf00058926
The paper
Whitehead, S. D., & Ballard, D. H. (1991). Learning to perceive and act by trial and error. Machine Learning. https://doi.org/10.1007/bf00058926