A Data-Driven Simulator for Assessing Decision-Making in Soccer

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2021

Autores

Tiago Mendes Neves (Autor) Outra; João Mendes Moreira (Autor) FEUP; Rosaldo J. F. Rossetti (Autor) FEUP

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2021)

Páginas: 687-698

20th EPIA Conference on Artificial Intelligence (EPIA)

ELECTR NETWORK, SEP 07-09, 2021

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 5 Citações

Outras Informações

ID Authenticus: P-00V-CMM

DOI: 10.1007/978-3-030-86230-5_54

Abstract (EN): Decision-making is one of the crucial factors in soccer (association football). The current focus is on analyzing data sets rather than posing what if questions about the game. We propose simulation-based methods that allow us to answer these questions. To avoid simulating complex human physics and ball interactions, we use data to build machine learning models that form the basis of an event-based soccer simulator. This simulator is compatible with the OpenAI GYM API. We introduce tools that allow us to explore and gather insights about soccer, like (1) calculating the risk/reward ratios for sequences of actions, (2) manually defining playing criteria, and (3) discovering strategies through Reinforcement Learning.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12