Learning Foraging Thresholds for Lizards:
This paper gives a proof of convergence for a learning algorithm that
describes how anoles (lizards found in the Caribbean) learn a
foraging threshold distance. An anole will pursue a prey if and
only if it is within this threshold of the anole's perch. The
learning algorithm was proposed by Roughgarden and his colleagues.
They experimentally determined that this algorithm quickly converges
to the foraging threshold that is predicted by optimal foraging
theory. We provide analytic confirmation that the optimal foraging
behavior as predicted by Roughgarden's model can be attained by a
lizard that follows this simple and zoologically plausible rule of
An Analysis of a Simple Learning Algorithm
Journal of Theoretical Biology, 197:361--369.
A preliminary version appeared in Proceedings of the 9th Conference on
Computational Learning Theory, pp. 2--9, 1996, and is
Copyright © 1996 by ACM, Inc.