An ant-inspired approach to mathematical sampling

Date:
June 19, 2020
Source:
University of Bristol
Summary:
Researchers have observed the exploratory behavior of ants to inform
the development of a more efficient mathematical sampling technique.



FULL STORY ==========================================================================
In a paper published by the Royal Society, a team of Bristol researchers observed the exploratory behaviour of ants to inform the development of
a more efficient mathematical sampling technique.


========================================================================== Animals like ants have the challenge of exploring their environment
to look for food and potential places to live. With a large group of individuals, like an ant colony, a large amount of time would be wasted
if the ants repeatedly explored the same empty areas.

The interdisciplinary team from the University of Bristol's Faculties
of Engineering and Life Sciences, predicted that the study species --
the 'rock ant' -- uses some form of chemical communication to avoid
exploring the same space multiple times.

Lead author, Dr Edmund Hunt, said: "This would be a reversal of the
Hansel and Gretel story -- instead of following each other's trails,
they would avoid them in order to explore collectively.

"To test this theory, we conducted an experiment where we let ants
explore an empty arena one by one. In the first condition, we cleaned the
arena between each ant so they could not leave behind any trace of their
path. In the second condition, we did not clean between ants. The ants in
the second condition (no cleaning) made a better exploration of the arena
-- they covered more space." In mathematics, a probability distribution describes how likely are each of a set of different possible outcomes:
for example, the chance that an ant will find food at a certain place. In
many science and engineering problems, these distributions are highly
complex, and they do not have a neat mathematical description. Instead,
one must sample from it to obtain a good approximation: with a desire
to avoid sampling too much from unimportant (low probability) parts of
the distribution.

The team wanted to find out if adopting an ant-inspired approach would
hasten this sampling process.

"We predicted that we could simulate the approach adopted by the ants in
the mathematical sampling problem, by leaving behind a 'negative trail'
of where has already been sampled. We found that our ant-inspired sampling method was more efficient (faster) than a standard method which does
not leave a memory of where has already been sampled," said Dr Hunt.

These findings contribute toward an interesting parallel between the exploration problem confronted by the ants, and the mathematical sampling problem of acquiring information. This parallel can inform our fundamental understanding of what the ants have evolved to do: acquire information
more efficiently.

"Our ant-inspired sampling method may be useful in many domains,
such as computational biology, for speeding up the analysis of complex problems. By describing the ants' collective behaviour in informational
terms, it also allows us to quantify how helpful are different aspects of
their behaviour to their success. For example, how much better do they
perform when their pheromones are not cleaned away. This could allow us
to make predictions about which behavioural mechanisms are most likely
to be favoured by natural selection."

========================================================================== Story Source: Materials provided by University_of_Bristol. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Edmund R. Hunt, Nigel R. Franks, Roland J. Baddeley. The Bayesian
superorganism: externalized memories facilitate distributed
sampling.

Journal of The Royal Society Interface, 2020; 17 (167): 20190848
DOI: 10.1098/rsif.2019.0848 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/06/200619094203.htm

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