CoChat
Sign Up
Cited by 81Reinforcement Learning in RoboticsAI-based Problem Solving and PlanningMachine Learning and Algorithms

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Active Perception and Reinforcement Learning

Whitehead, Steven D., Ballard, Dana H.·1990·Machine Learning Proceedings 1990
DOI 10.1016/b978-1-55860-141-3.50025-0
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1016/b978-1-55860-141-3.50025-0

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Ballard (1991) and Whitehead and Ballard (1990) demonstrated that perception is an active process of information acquisition, with vision dependent on action strategies rather than passive encoding.

    “Ballard (1991) and Whitehead and Ballard (1990) further demonstrated that perception is an active process of information acquisition: by moving the eyes or body to gather task-relevant information, the agent learns control policies that determine what is sensed, making vision dependent on action strategies rather than passive encoding.”

    From Toward Enactive Artificial Intelligence (2026) · cites this paper as Whitehead and Ballard (1990)

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 26

  1. Agre, 1987, Pengi: An implementation of a theory of activity, AAAI, 268
  2. Philip E. Agre. The Dynamic Structure of Everyday Life. PhD thesis, MIT Artificial Intelligence Lab., 1988. (Tech Report No. 1085).
  3. Charles W. Anderson. Towers of hanoi with connectionist networks: learning new features. In Proceedings of the Sixth International Conf. on Machine Learning, pages 345–350, Ithaca, NY, 1989. Morgan Kaufmann. · DOI
  4. Barto, 1981, Landmark learning: An illustration of associative search, Biological Cybernetics, 42, 1, 10.1007/BF00335152 · DOI
  5. Barto, 1983, Neuron-like elements that can solve difficult learning control problems, IEEE Trans, on Systems, Man, and Cybernetics, SMC-13, 834, 10.1109/TSMC.1983.6313077 · DOI
  6. Andrew B. Barto, Richard S. Sutton, and Chris Watkins. Sequential decision problems and neural networks. In Proceedings of 1989 Conf. on Neural Information Processing, 1989
  7. Jim Blythe and Tom M. Mitchell. On becoming reactive. In Proceedings of the Sixth Intl. Conf. on Machine Learning, pages 255–259, 1989 · DOI
  8. Lashon B. Booker. Intelligent behavior as an adaptation to the task environment. PhD thesis, University of Michigan, 1982
  9. Chapman, 1989, Penguins can make cake, AI Magazine, 10, 45
  10. W. F. Clocksin and A. W. Moore. Some experiments in adaptive state-space robotics. Technical report, Computer Laboratory, University of Cambridge, October 1988
  11. Mark Drummond. Situated control rules. In Proceedings of the Rochester Planning Workshop, 1989. (Also Univ. of Rochester Techical Report No. 284).
  12. Fikes, 1972, Learning and executing generalized robot plans, Artifical Intelligence, 3, 251, 10.1016/0004-3702(72)90051-3 · DOI
  13. R. James Firby. An investegation into reactive planning in complex domains. In AAAI, pages 202–206, 1987
  14. Michael P. Georgeff and Amy L. Lansky. Reactive reasoning and planning. In AAAI, pages 677–682, 1987
  15. Leslie P. Kaelbling. A formal framework for learning in embedded systems. In Proceedings of the Sixth International Workshop on Machine Learning, pages 350–353, 1989 · DOI
  16. Laird, 1986, Chunking in soar: The anatomy of a general learning mechanism, Machine Learning, 1, 11, 10.1023/A:1022639103969 · DOI
  17. Nils J. Nilsson. Action networks. In Proceedings of the Rochester Planning Workshop, 1989. (Also Univ. of Rochester Techical Report No. 284).
  18. Marcel. J. Schoppers. Universal plans for reactive robots in unpredictable domains. In Proceedings of IJCAI-87, 1987
  19. Marcel J. Schoppers. Representation and Automatic Synthesis of Reaction Plans. PhD thesis, Dept, of Computer Science, University of Illinois at Urbana-Champaign, 1989
  20. Richard S. Sutton. Temporal Credit Assignment In Reinforcement Learning. PhD thesis, University of Massachusetts at Amherst, 1984. (Also COINS Tech Report 84–02).
  21. Richard S. Sutton. First results with DYNA, an integrated architecture for learning, planning, and reacting. In Proceedings of the AAAI Spring Symposium on Planning in Uncertain, Unpredictable, or Changing Environments, 1990 · DOI
  22. Chris Watkins. Learning from delayed rewards. PhD thesis, Cambridge University, 1989
  23. Steven D. Whitehead and Dana H. Ballard. Reactive behavior, learning, and anticipation. In Proceedings of the NASA Conference on Space Telerobotics, Pasadena, CA, 1989
  24. Steven D. Whitehead and Dana H. Ballard. A role for anticipation in reactive systems that learn. In Proceedings of the Sixth International Conference on Machine Learning, Ithaca, NY, 1989. Morgan Kaufmann. · DOI
  25. Wilson, 1987, Classifier systems and the animate problem, Machine Learning, 2, 199, 10.1023/A:1022655214215 · DOI
  26. Stewart W. Wilson. Hierarchical credit allocation in a classifier system. In Proceedings of the Tenth IJCAI, pages 217–220. Morgan Kaufmann, 1987

Cited by

Sorted by citations

Metadata

Authors
Whitehead, S. D., & Ballard, D. H.
Affiliations
University of Rochester (US)
Venue
Machine Learning Proceedings 1990
Publisher
Elsevier
Published
1990
Pages
179-188
DOI
10.1016/b978-1-55860-141-3.50025-0
OpenAlex
W1500024457
Cited by
81
References
3
Citations by year
2026: 2 · 2023: 1 · 2022: 1 · 2021: 1 · 2020: 2 · 2019: 2 · 2017: 1
FWCI
12.49
Type
book chapter
Language
en
Open access
closed
Licence
Locations
Elsevier eBooks (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, reinforcement learning, perceptual aliasing, adaptive control architecture, sensorimotor system, attentional focus, decision-making systems

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 ›

Are you an author, or do you see a problem with this page? Request a review