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Conference Papers Year : 2020

Finding Flow in Training Activities by Exploring Single-Agent Arcade Game Information Dynamics

Abstract

This paper incorporates discussion about game refinement theory and the flow model to analyze simulation data collected from two types of arcade games. A mathematical model of the arcade game processes is formulated. The essence of the arcade games is verified through the game-playing processes of players. In particular, challenge setup could contribute to the addictiveness when the mode is close to flow channel. Risk frequency ratio is applied to measure the process and verified the more entertaining mode of training activities.
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hal-03686013 , version 1 (02-06-2022)

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Yuexian Gao, Naying Gao, Mohd Khalid, Hiroyuki Iida. Finding Flow in Training Activities by Exploring Single-Agent Arcade Game Information Dynamics. 19th International Conference on Entertainment Computing (ICEC), Nov 2020, Xi'an, China. pp.126-133, ⟨10.1007/978-3-030-65736-9_11⟩. ⟨hal-03686013⟩
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