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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/6714C84A-92A6-489A-B263-2B0EC8C7DC9F | ||
| Year | 2021 | ||
| Type of Item | Conference Publication | ||
| License |
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| Bibliographic Citation | 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. https://doi.org/10.48550/arXiv.2102.09469 | ||
| Appears in Collections |
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 Carloand 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 760matches shows that by using optimised tactics with our fluentobjective and prior games, we can on average increase teams meanexpected finishing distribution in the league by up to 35.6%.
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