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Ιδρυματικό Αποθετήριο [SANDBOX]
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| URI | http://purl.tuc.gr/dl/dias/6714C84A-92A6-489A-B263-2B0EC8C7DC9F | - |
| Αναγνωριστικό | https://doi.org/10.48550/arXiv.2102.09469 | - |
| Αναγνωριστικό | https://arxiv.org/abs/2102.09469 | - |
| Αναγνωριστικό | https://dl.acm.org/doi/10.5555/3463952.3463981 | - |
| Γλώσσα | en | - |
| Μέγεθος | 9 pages | en |
| Τίτλος | Optimising long-term outcomes using real-world fluent objectives: an application to football | en |
| Δημιουργός | Beal Ryan J. | en |
| Δημιουργός | Chalkiadakis Georgios | en |
| Δημιουργός | Χαλκιαδακης Γεωργιος | el |
| Δημιουργός | Norman Timothy J. | en |
| Δημιουργός | Ramchurn, Sarvapali Dyanand | en |
| Εκδότης | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) | en |
| Περίληψη | In this paper, we present a novel approach for optimising longterm tactical and strategic decision-making in football (soccer) by encapsulating events in a league environment across a given time frame. We model the teams’ objectives for a season and track how these evolve as games unfold to give a fluent objective that can aid in decision-making games. We develop Markov chain Monte Carlo and deep learning-based algorithms that make use of the fluent objectives in order to learn from prior games and other games in the environment and increase the teams’ long-term performance. Simulations of our approach using real-world datasets from 760 matches shows that by using optimised tactics with our fluent objective and prior games, we can on average increase teams mean expected finishing distribution in the league by up to 35.6%. | en |
| Τύπος | Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Publication | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2023-07-13 | - |
| Ημερομηνία Δημοσίευσης | 2021 | - |
| Θεματική Κατηγορία | Artificial intelligence | en |
| Θεματική Κατηγορία | Computer science and game theory | en |
| Βιβλιογραφική Αναφορά | R. Beal, G. Chalkiadakis, T. J. Norman, and S. D. Ramchurn, “Optimising long-term outcomes using real-world fluent objectives: an application to football,” in Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), virtual event, 2021, vol. 1, pp. 196-204, doi: 10.48550/arXiv.2102.09469. | en |
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