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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/uo6yx5jpgnf2zl7mkrumytd4ti" style="color: black;">Proceedings of the 2003 ACM symposium on Applied computing - SAC '03</a>
Researchers building multi-agent algorithms typically work with problems abstracted away from real applications. The abstracted problem instances allow systematic and detailed investigations of new algorithms. However, a key question is how to apply algorithm, developed on an abstract problem, in a real application. In this paper, we report on what was required to apply a particular distributed resource allocation algorithm developed for an abstract coordination problem in a real hardware<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/952532.952543">doi:10.1145/952532.952543</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/sac/ScerriMST03.html">dblp:conf/sac/ScerriMST03</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p66tjbnswvhspl7dy3e5tmgqka">fatcat:p66tjbnswvhspl7dy3e5tmgqka</a> </span>
more »... ation. A probabilistic representation of resources and tasks was used to deal with uncertainty and dynamics and local reasoning was used to deal with delays in the distributed resource allocation algorithm. The probabilistic representation and local reasoning enabled the use of the multi-agent algorithm which, in turn, improved the overall performance of the system.
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