Effective team strategies and joint decision-making processes are fundamental in modern robotic applications, where multiple units have to cooperate to achieve a common goal. The research community in artificial intelligence and robotics has launched robotic competitions to promote research and validate new approaches, by providing robust benchmarks to evaluate all the components of a multiagent system—ranging from hardware to high-level strategy learning. Among these competitions RoboCup has a prominent role, running one of the first worldwide multirobot competition (in the late 1990s), challenging researchers to develop robotic systems able to compete in the game of soccer. Robotic soccer teams are complex multirobot systems, where each unit shows individual skills, and solid teamwork by exchanging information about their local perceptions and intentions. In this survey, we dive into the techniques developed within the RoboCup framework by analyzing and commenting on them in detail. We highlight significant trends in the research conducted in the field and to provide commentaries and insights, about challenges and achievements in generating decision-making processes for multirobot adversarial scenarios. As an outcome, we provide an overview a body of work that lies at the intersection of three disciplines: Artificial intelligence, robotics, and games.

Game Strategies for Physical Robot Soccer Players: A Survey / Antonioni, Emanuele; Suriani, Vincenzo; Riccio, Francesco; Nardi, Daniele. - In: IEEE TRANSACTIONS ON GAMES. - ISSN 2475-1510. - 13:4(2021), pp. 342-357. [10.1109/TG.2021.3075065]

Game Strategies for Physical Robot Soccer Players: A Survey

Antonioni, Emanuele
Co-primo
;
Suriani, Vincenzo
Co-primo
;
Riccio, Francesco;Nardi, Daniele
2021

Abstract

Effective team strategies and joint decision-making processes are fundamental in modern robotic applications, where multiple units have to cooperate to achieve a common goal. The research community in artificial intelligence and robotics has launched robotic competitions to promote research and validate new approaches, by providing robust benchmarks to evaluate all the components of a multiagent system—ranging from hardware to high-level strategy learning. Among these competitions RoboCup has a prominent role, running one of the first worldwide multirobot competition (in the late 1990s), challenging researchers to develop robotic systems able to compete in the game of soccer. Robotic soccer teams are complex multirobot systems, where each unit shows individual skills, and solid teamwork by exchanging information about their local perceptions and intentions. In this survey, we dive into the techniques developed within the RoboCup framework by analyzing and commenting on them in detail. We highlight significant trends in the research conducted in the field and to provide commentaries and insights, about challenges and achievements in generating decision-making processes for multirobot adversarial scenarios. As an outcome, we provide an overview a body of work that lies at the intersection of three disciplines: Artificial intelligence, robotics, and games.
2021
Robotic competition; soccer robots; strategies in robotic games;
01 Pubblicazione su rivista::01a Articolo in rivista
Game Strategies for Physical Robot Soccer Players: A Survey / Antonioni, Emanuele; Suriani, Vincenzo; Riccio, Francesco; Nardi, Daniele. - In: IEEE TRANSACTIONS ON GAMES. - ISSN 2475-1510. - 13:4(2021), pp. 342-357. [10.1109/TG.2021.3075065]
File allegati a questo prodotto
File Dimensione Formato  
Antonioni_Game_Strategies_2021.pdf

accesso aperto

Note: 10.1109/TG.2021.3075065
Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Creative commons
Dimensione 2.87 MB
Formato Adobe PDF
2.87 MB Adobe PDF
Antonioni_Game_2021.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.87 MB
Formato Adobe PDF
2.87 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1620604
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 13
  • ???jsp.display-item.citation.isi??? 12
social impact