research-article

Gaming Ecosystems for Education and Research: Where Artificial Intelligence meets with Software Engineering, at scale

Authors:

Dimitrios Kalles,

Kyriakos Giagtzoglou,

Konstantinos MitropoulosAuthors Info & Claims

MEDES '19: Proceedings of the 11th International Conference on Management of Digital EcoSystems

Pages 199 - 204

https://doi.org/10.1145/3297662.3365811

Published: 10 January 2020 Publication History

Abstract

We present aspects of ecosystem engineering for a strategy board game. Human and machine players of the ecosystem can pick opponents or form teams and play against other teams or players. We present the key features of the ecosystem, the highlights of the development process and we propose concrete potential uses of the ecosystem in research and education.

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Cited By

Mitropoulos KVasilopoulos GGiagtzoglou KKalles D(2022)An experimental comparison of single-agent and multi-agent learning-and-playing algorithms in a gaming ecosystemIntelligent Decision Technologies10.3233/IDT-21021216:2(431-439)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/IDT-210212

Index Terms

Gaming Ecosystems for Education and Research: Where Artificial Intelligence meets with Software Engineering, at scale

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An experimental comparison of single-agent and multi-agent learning-and-playing algorithms in a gaming ecosystem

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Information & Contributors

Information

Published In

cover image ACM Other conferences

MEDES '19: Proceedings of the 11th International Conference on Management of Digital EcoSystems

November 2019

350 pages

ISBN:9781450362382

DOI:10.1145/3297662

General Chairs:
Richard Chbeir
University of Pau and Pays de l'Adour, France
,
Yannis Manolopoulos
Open University of Cyprus, Cyprus
,
Program Chairs:
Sergio Ilarri
University of Zaragoza, Spain
,
Apostolos Papadopoulos
Aristotle University of Thessaloniki, Greece

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 January 2020

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Research-article
Research
Refereed limited

Funding Sources

Regional Operational Program of Western Greece 2014-2020 and European Regional Development Fund (ERDF)

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MEDES '19

MEDES '19: 11th International Conference on Management of Digital EcoSystems

November 12 - 14, 2019

Limassol, Cyprus

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MEDES '19 Paper Acceptance Rate 41 of 102 submissions, 40%;

Overall Acceptance Rate 267 of 682 submissions, 39%

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Cited By

Mitropoulos KVasilopoulos GGiagtzoglou KKalles D(2022)An experimental comparison of single-agent and multi-agent learning-and-playing algorithms in a gaming ecosystemIntelligent Decision Technologies10.3233/IDT-21021216:2(431-439)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/IDT-210212

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