Abstract
Hanabi is a cooperative game for ordering cards through information exchange, and has been studied from various cooperation aspects, such as self-estimation, psychology, and communication theory. Cooperation is achieved in terms of not only increased scores, but also reduced cognitive load for the players. Therefore, while evaluating AI agents playing a cooperative game, evaluation indexes other than scores must be considered. In this study, an agent algorithm was developed that follows the human thought process for guessing the AI’s strategy by utilizing the length of thinking time of the human player and changing the estimation reliability, and the influence of this agent on game scores, cognitive load, and human impressions of the agent was investigated. Thus, thinking time was used as an indicator of cognitive load, and the results showed that it is inversely proportional to the confidence of choice. Furthermore, it was found that the mean thinking time of the human player was shortened when the agent used the thinking time of the player, as compared with the estimation of the conventional agent, and this did not affect human impression. There was no significant difference in the achieved score and success rate of the estimation by changing the estimation reliability according to the thinking time. The above results suggest that the agent developed in this study could reduce the cognitive load of the players without influencing performance.
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This research was supported by JSPS Research Grants JP26118006, JP18KT0029.
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Sato, E., Osawa, H. (2020). Reducing Partner’s Cognitive Load by Estimating the Level of Understanding in the Cooperative Game Hanabi. In: Cazenave, T., van den Herik, J., Saffidine, A., Wu, IC. (eds) Advances in Computer Games. ACG 2019. Lecture Notes in Computer Science(), vol 12516. Springer, Cham. https://doi.org/10.1007/978-3-030-65883-0_2
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