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Learning the value of teamwork to form efficient teams

Learning the value of teamwork to form efficient teams
Learning the value of teamwork to form efficient teams
In this paper we describe a novel approach to team formation based on the value of inter-agent interactions. Specifically, we propose a model of teamwork that considers outcomes from chains of interactions between agents. Based on our model, we devise a number of network metrics to capture the contribution of interactions between agents. This is then used to learn the value of teamwork from historical team performance data. We apply our model to predict team performance and validate our approach using real-world team performance data from the 2018 FIFA World Cup. Our model is shown to better predict the real-world performance of teams by up to 46% compared to models that ignore inter-agent interactions.
Beal, Ryan, James
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Changder, Narayan
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Norman, Timothy
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Ramchurn, Sarvapali
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Beal, Ryan, James
d9874cb0-bd92-4a16-8576-78d769b41ff7
Changder, Narayan
f891fa68-68e4-4d2b-9c0b-a04f7e3cf540
Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3

Beal, Ryan, James, Changder, Narayan, Norman, Timothy and Ramchurn, Sarvapali (2019) Learning the value of teamwork to form efficient teams. The Thirty-Fourth AAAI Conference on Artificial Intelligence, Hilton Midtown New York, United States. 07 - 12 Feb 2020. (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper we describe a novel approach to team formation based on the value of inter-agent interactions. Specifically, we propose a model of teamwork that considers outcomes from chains of interactions between agents. Based on our model, we devise a number of network metrics to capture the contribution of interactions between agents. This is then used to learn the value of teamwork from historical team performance data. We apply our model to predict team performance and validate our approach using real-world team performance data from the 2018 FIFA World Cup. Our model is shown to better predict the real-world performance of teams by up to 46% compared to models that ignore inter-agent interactions.

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More information

Accepted/In Press date: 11 November 2019
Venue - Dates: The Thirty-Fourth AAAI Conference on Artificial Intelligence, Hilton Midtown New York, United States, 2020-02-07 - 2020-02-12

Identifiers

Local EPrints ID: 436234
URI: http://eprints.soton.ac.uk/id/eprint/436234
PURE UUID: e15e6827-6823-4c3b-838a-95e8f0c43013
ORCID for Timothy Norman: ORCID iD orcid.org/0000-0002-6387-4034
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 04 Dec 2019 17:30
Last modified: 15 Aug 2020 01:43

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Contributors

Author: Ryan, James Beal
Author: Narayan Changder
Author: Timothy Norman ORCID iD
Author: Sarvapali Ramchurn ORCID iD

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