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Asaf Shiloni, Noa Agmon, and Gal
A. Kaminka. Of Robot Ants and Elephants. In Proceedings of the Eighth International Joint Conference on Autonomous
Agents and Multi-Agent Systems (AAMAS-09), 2009.
Investigations of multi-robot systems often make implicit assumptions concerning the computational capabilities of the robots. Despite the lack of explicit attention to the computational capabilities of robots, two computational classes of robots emerge as focal points of recent research: Robot Ants and robot Elephants. Ants have poor memory and communication capabilities, but are able to communicate using pheromones, in effect turning their work area into a shared memory. By comparison, elephants are computationally stronger, have large memory, and are equipped with strong sensing and communication capabilities. Unfortunately, not much is known about the relation betweenthe capabilities of these models in terms of the tasks they can address.In this paper, we present formal models of both ants and elephants, andinvestigate if one dominates the other. We present two algorithms:AntEater, which allows elephant robots to execute ant algorithms; andElephantGun, which converts elephant algorithms---specified as Turingmachines---into ant algorithms. By exploring the computational capabilitiesof these algorithms, we reach interesting conclusions regarding the computationalpower of both models.
@InProceedings{aamas09,
author = {Asaf Shiloni and Noa Agmon and Gal A. Kaminka},
title = {Of Robot Ants and Elephants},
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booktitle = AAMAS-09,
OPTpages = {},
year = {2009},
abstract = { Investigations of multi-robot systems often make implicit
assumptions concerning the computational capabilities of the robots.
Despite the lack of explicit attention to the computational capabilities of
robots, two \emph{computational classes} of robots emerge as focal points
of recent research: Robot \emph{Ants} and robot \emph{Elephants}. Ants have
poor memory and communication capabilities, but are able to communicate using pheromones, in effect
turning their work area into a shared memory. By comparison, elephants are computationally stronger,
have large memory, and are equipped with strong sensing and communication capabilities.
Unfortunately, not much is known about the relation between
the capabilities of these models in terms of the tasks they can address.
In this paper, we present formal models of both ants and elephants, and
investigate if one dominates the other. We present two algorithms:
\emph{AntEater}, which allows elephant robots to execute ant algorithms; and
\emph{ElephantGun}, which converts elephant algorithms---specified as Turing
machines---into ant algorithms. By exploring the computational capabilities
of these algorithms, we reach interesting conclusions regarding the computational
power of both models. },
wwwnote = {},
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OPTvolume = {},
OPTnumber = {},
OPTseries = {},
OPTaddress = {},
OPTmonth = {},
OPTorganization = {},
OPTpublisher = {},
OPTnote = {},
OPTnote = {},
OPTannote = {}
}
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