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Advances in miniaturization will allow for the commoditization of tiny satellites, known as ``CubeSats." This commoditization in addition to reducing price and increasing the numbers of satellites, will also result in the ``democratization" of small space missions where numerous institutions can launch their own satellites. However, current algorithms made for small tightly-managed space missions are ill-designed to take advantage of the huge amount of resources available in a decentralized collection of CubeSats. We believe that multiagent evolutionary algorithms are ideally suited to exploit the distributed nature of this new problem. This paper presents a solution where a customer in need of satellite observations can reliably obtain these observations at low cost, through the help of a multiagent system as an intermediary. Each agent in this system is assigned to a single CubeSat. Given a set of the customer's observational needs, and models of the CubeSats' salient properties, the agents evolve policies that attempt to purchase an appropriate set of observations at a low price. This system is especially flexible as it demands no centralized resource broker, contracts or commitments of resources. We perform a series of experiments on an Earth-observation domain. The results show that the evolutionary methods combined with multiagent techniques have three times the performance of a simple hand-coded allocation algorithm, and twice the performance of simple evolving agents.
@inproceedings{tumer-holmesparker-sat_gecco12,
author = {C. Holmes Parker and A. Agogino and K. Tumer},
title = {Evolving Distributed Resource Sharing for CubeSat Constellations},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
month = {July},
address = {Philadelphia, PA},
abstract={Advances in miniaturization will allow for the commoditization of tiny satellites, known as ``CubeSats." This commoditization in addition to reducing price and increasing the numbers of satellites, will also result in the ``democratization" of small space missions where numerous institutions can launch their own satellites. However, current algorithms made for small tightly-managed space missions are ill-designed to take advantage of the huge amount of resources available in a decentralized collection of CubeSats. We believe that multiagent evolutionary algorithms are ideally suited to exploit the distributed nature of this new problem. This paper presents a solution where a customer in need of satellite observations can reliably obtain these observations at low cost, through the help of a multiagent system as an intermediary. Each agent in this system is assigned to a single CubeSat. Given a set of the customer's observational needs, and models of the CubeSats' salient properties, the agents evolve policies that attempt to purchase an appropriate set of observations at a low price. This system is especially flexible as it demands no centralized resource broker, contracts or commitments of resources. We perform a series of experiments on an Earth-observation domain. The results show that the evolutionary methods combined with multiagent techniques have three times the performance of a simple hand-coded allocation algorithm, and twice the performance of simple evolving agents.},
bib2html_pubtype = {Refereed Conference Papers},
bib2html_rescat = {Evolutionary Algorithms, Multiagent Systems},
year = {2012}
}
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