Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | GECCO '23 Companion |
| Untertitel | Proceedings of the Companion Conference on Genetic and Evolutionary Computation |
| Seiten | 243-246 |
| Seitenumfang | 4 |
| ISBN (elektronisch) | 9798400701207 |
| Publikationsstatus | Veröffentlicht - 24 Juli 2023 |
| Veranstaltung | 2023 Genetic and Evolutionary Computation Conference Companion: GECCO 2023 - Lisbon, Portugal Dauer: 15 Juli 2023 → 19 Juli 2023 |
Abstract
A methodology on how to prepare agents to succeed on a priori unknown logistics problems is presented. The training of the agents is and can only be executed using a small number of test problems that are taken out of a broad class of generalized logistics problems. The developed agents are then evaluated on unknown instances of the problem class. This work has been developed in the context of last year’s AbstractSwarm Multi-Agent Logistics Competition. The most successful algorithms are presented, and additionally, all participating algorithms are discussed with respect to the features of the algorithms that contribute to their success. As a result, we conclude that such a broad variety of a priori unknown logistics problems can be solved efficiently if multiple different good working approaches are used, instead of trying to find one optimal algorithm. For the used test problems this method can undercut, trivial as well as non-trivial implementations, for example, algorithms based on machine learning.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Theoretische Informatik und Mathematik
- Informatik (insg.)
- Angewandte Informatik
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GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. 2023. S. 243-246.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Training agents for unknown logistics problems
AU - Schmid, Elisa
AU - Becker, Matthias
PY - 2023/7/24
Y1 - 2023/7/24
N2 - A methodology on how to prepare agents to succeed on a priori unknown logistics problems is presented. The training of the agents is and can only be executed using a small number of test problems that are taken out of a broad class of generalized logistics problems. The developed agents are then evaluated on unknown instances of the problem class. This work has been developed in the context of last year’s AbstractSwarm Multi-Agent Logistics Competition. The most successful algorithms are presented, and additionally, all participating algorithms are discussed with respect to the features of the algorithms that contribute to their success. As a result, we conclude that such a broad variety of a priori unknown logistics problems can be solved efficiently if multiple different good working approaches are used, instead of trying to find one optimal algorithm. For the used test problems this method can undercut, trivial as well as non-trivial implementations, for example, algorithms based on machine learning.
AB - A methodology on how to prepare agents to succeed on a priori unknown logistics problems is presented. The training of the agents is and can only be executed using a small number of test problems that are taken out of a broad class of generalized logistics problems. The developed agents are then evaluated on unknown instances of the problem class. This work has been developed in the context of last year’s AbstractSwarm Multi-Agent Logistics Competition. The most successful algorithms are presented, and additionally, all participating algorithms are discussed with respect to the features of the algorithms that contribute to their success. As a result, we conclude that such a broad variety of a priori unknown logistics problems can be solved efficiently if multiple different good working approaches are used, instead of trying to find one optimal algorithm. For the used test problems this method can undercut, trivial as well as non-trivial implementations, for example, algorithms based on machine learning.
KW - agent learning
KW - competition
KW - unknown logistics problems
UR - http://www.scopus.com/inward/record.url?scp=85169034992&partnerID=8YFLogxK
U2 - 10.1145/3583133.3590724
DO - 10.1145/3583133.3590724
M3 - Conference contribution
AN - SCOPUS:85169034992
SP - 243
EP - 246
BT - GECCO '23 Companion
T2 - 2023 Genetic and Evolutionary Computation Conference Companion
Y2 - 15 July 2023 through 19 July 2023
ER -