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Learning foraging thresholds for lizards
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Goldberg, Leslie Ann, Hart, W. E. and Wilson, D. B. (1996) Learning foraging thresholds for lizards. University of Warwick. Department of Computer Science. (Department of Computer Science research report). (Unpublished)
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Abstract
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. The 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 converges to this optimal foraging threshold with high probability.
Item Type: | Report | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||
Library of Congress Subject Headings (LCSH): | Anoles -- Mathematical models | ||||
Series Name: | Department of Computer Science research report | ||||
Publisher: | University of Warwick. Department of Computer Science | ||||
Official Date: | January 1996 | ||||
Dates: |
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Number: | Number 296 | ||||
Number of Pages: | 11 | ||||
DOI: | CS-RR-296 | ||||
Institution: | University of Warwick | ||||
Theses Department: | Department of Computer Science | ||||
Status: | Not Peer Reviewed | ||||
Publication Status: | Unpublished | ||||
Reuse Statement (publisher, data, author rights): | L.A. Goldberg, W.E. Hart and D.B. Wilson, “Analysis of a simple learning algorithm: Learning foraging thresholds for lizards”, <i>Proceedings of COLT</i>, pp. 2-9 (1996) | ||||
Funder: | United States. Department of Energy, European Strategic Programme of Research and Development in Information Technology (ESPRIT), United States. National Security Agency (USNSA), National Science Foundation (U.S.) (NSF) | ||||
Grant number: | DE-AC04-94AL85000 (DOE), 7141 (ESPRIT), MDA904-92-H-3060 (NSA), DMS 9206374 (NSF) | ||||
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