Learning foraging thresholds for lizards

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Description

This work gives a proof of convergence for a randomized learning algorithm that describes how anoles (lizards found in the Carribean) learn a foraging threshold distance. This model assumes that an anole will pursue a prey if and only if it is within this threshold of the anole`s perch. This learning algorithm was proposed by the biologist Roughgarden and his colleagues. They experimentally confirmed that this algorithm quickly converges to the foraging threshold that is predicted by optimal foraging theory our analysis provides an analytic confirmation that the learning algorithm converses to this optimal foraging threshold with high probability.

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11 p.

Creation Information

Goldberg, L.A.; Hart, W.E. & Wilson, D.B. January 12, 1996.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

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Authors

  • Goldberg, L.A. Univ. of Warwick, Coventry (United Kingdom). Dept. of Computer Science
  • Hart, W.E. Sandia National Labs., Albuquerque, NM (United States)
  • Wilson, D.B. Massachusetts Inst. of Tech., Cambridge, MA (United States)

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM (United States)
    Place of Publication: Albuquerque, New Mexico

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Description

This work gives a proof of convergence for a randomized learning algorithm that describes how anoles (lizards found in the Carribean) learn a foraging threshold distance. This model assumes that an anole will pursue a prey if and only if it is within this threshold of the anole`s perch. This learning algorithm was proposed by the biologist Roughgarden and his colleagues. They experimentally confirmed that this algorithm quickly converges to the foraging threshold that is predicted by optimal foraging theory our analysis provides an analytic confirmation that the learning algorithm converses to this optimal foraging threshold with high probability.

Physical Description

11 p.

Notes

OSTI as DE96007326

Source

  • 9. annual conference on computational learning theory, Desenzano del Garda (Italy), 23 Jun 1996

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Identifier

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  • Other: DE96007326
  • Report No.: SAND--96-0257C
  • Report No.: CONF-9606113--1
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 225044
  • Archival Resource Key: ark:/67531/metadc669909

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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Creation Date

  • January 12, 1996

Added to The UNT Digital Library

  • June 29, 2015, 9:42 p.m.

Description Last Updated

  • April 13, 2016, 2:45 p.m.

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Citations, Rights, Re-Use

Goldberg, L.A.; Hart, W.E. & Wilson, D.B. Learning foraging thresholds for lizards, article, January 12, 1996; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc669909/: accessed December 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.