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GAIM: Game Action Information Mining Framework for Multiplayer Online Card Games (Rummy as Case Study)
We introduce GAIM, a deep-learning analytical framework that enables benchmarking and profiling of players, from the perspective of how the players react to the game state and evolution of games. In particular, we focus on multi-player, skill-based card games, and use Rummy as a case study. GAIM fra...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206269/ http://dx.doi.org/10.1007/978-3-030-47436-2_33 |
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author | Eswaran, Sharanya Vimal, Vikram Seth, Deepanshi Mukherjee, Tridib |
author_facet | Eswaran, Sharanya Vimal, Vikram Seth, Deepanshi Mukherjee, Tridib |
author_sort | Eswaran, Sharanya |
collection | PubMed |
description | We introduce GAIM, a deep-learning analytical framework that enables benchmarking and profiling of players, from the perspective of how the players react to the game state and evolution of games. In particular, we focus on multi-player, skill-based card games, and use Rummy as a case study. GAIM framework provides a novel and extensible encapsulation of the game state as an image, and uses Convolutional Neural Networks (CNN) to learn these images to calibrate the goodness of the state, in such a way that the challenges arising from multiple players, chance factors and large state space, are all abstracted. We show that our model out-performs well-known image classification models, and also learns the nuances of the game without explicitly training with game-specific features, resulting in a true state model, wherein most of the misclassifications can be attributed to user mistakes or genuinely confusing hands. We show that GAIM helps gather fine-grained insights about player behavior, skill, tendencies, and business implications, that were otherwise not possible, thereby enabling targeted services and personalized player journeys. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this chapter (10.1007/978-3-030-47436-2_33) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-7206269 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72062692020-05-08 GAIM: Game Action Information Mining Framework for Multiplayer Online Card Games (Rummy as Case Study) Eswaran, Sharanya Vimal, Vikram Seth, Deepanshi Mukherjee, Tridib Advances in Knowledge Discovery and Data Mining Article We introduce GAIM, a deep-learning analytical framework that enables benchmarking and profiling of players, from the perspective of how the players react to the game state and evolution of games. In particular, we focus on multi-player, skill-based card games, and use Rummy as a case study. GAIM framework provides a novel and extensible encapsulation of the game state as an image, and uses Convolutional Neural Networks (CNN) to learn these images to calibrate the goodness of the state, in such a way that the challenges arising from multiple players, chance factors and large state space, are all abstracted. We show that our model out-performs well-known image classification models, and also learns the nuances of the game without explicitly training with game-specific features, resulting in a true state model, wherein most of the misclassifications can be attributed to user mistakes or genuinely confusing hands. We show that GAIM helps gather fine-grained insights about player behavior, skill, tendencies, and business implications, that were otherwise not possible, thereby enabling targeted services and personalized player journeys. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this chapter (10.1007/978-3-030-47436-2_33) contains supplementary material, which is available to authorized users. 2020-04-17 /pmc/articles/PMC7206269/ http://dx.doi.org/10.1007/978-3-030-47436-2_33 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Eswaran, Sharanya Vimal, Vikram Seth, Deepanshi Mukherjee, Tridib GAIM: Game Action Information Mining Framework for Multiplayer Online Card Games (Rummy as Case Study) |
title | GAIM: Game Action Information Mining Framework for Multiplayer Online Card Games (Rummy as Case Study) |
title_full | GAIM: Game Action Information Mining Framework for Multiplayer Online Card Games (Rummy as Case Study) |
title_fullStr | GAIM: Game Action Information Mining Framework for Multiplayer Online Card Games (Rummy as Case Study) |
title_full_unstemmed | GAIM: Game Action Information Mining Framework for Multiplayer Online Card Games (Rummy as Case Study) |
title_short | GAIM: Game Action Information Mining Framework for Multiplayer Online Card Games (Rummy as Case Study) |
title_sort | gaim: game action information mining framework for multiplayer online card games (rummy as case study) |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206269/ http://dx.doi.org/10.1007/978-3-030-47436-2_33 |
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