One of the most difficult tasks among social intelligence tasks that read the intentions of others is to estimate their own state from the actions of others. This behavior is one of the characteristics of human intelligence. For example, in the field of psychology, self-image that can not be seen by myself is called blind self and self-recognition of blind self is a measure of growth.
In this research, we used a cooperative game called Hanabi as an artificial intelligence task competing for such blind self. Hanabi is a cooperative type card game where all the agents cooperate to gather scores. All players cooperate with fireworks of five colors represented by rows of cards 1 to 5 to build up. And the size of this fireworks is the score. In this game, the player can not see his card, but can know all the agents’ cards other than his / her own card. Also, in this game communication between agents is restricted. Each agent must consume resources for information transmission in order to teach the number or color of cards of other players. No other communication means are prepared. I implemented an artificial intelligence agent to solve this Hanabi did. This agent can simulate the viewpoints and actions of others. By doing this, we examined how the reproduction of the viewpoint of others leads to scoring.

  • Kawagoe, Atsushi, and Hirotaka Osawa. 2020. “Concordance of Risk Tendency Increases User’s Satisfaction: Verification of Agents in Cooperative Game Hanabi.” In International Conference on Human-Agent Interaction, 140–48.
  • Sato, Eisuke, and Hirotaka Osawa. 2019. “Reducing Partner’s Cognitive Load by Estimating the Level of Understanding in the Cooperative Game Hanabi.” In Advances in Computer Games, 1–12.
  • Kato, Takuya, and Hirotaka Osawa. 2018. “I Know You Better Than You Know Yourself: Estimation of Blind Self Improves Acceptance for an Agent.” In Proceedings of the 6th International Conference on Human-Agent Interaction - HAI ’18, 144–52. New York, New York, USA: ACM Press. https://doi.org/10.1145/3284432.3284453.
  • Hirotaka Osawa. 2015. Solving Hanabi : Estimating Hands by Opponent’s Actions in Cooperative Game with Incomplete Information. In AAAI workshop: Computer Poker and Imperfect Information, 37–43.
  • 大澤博隆. 2015. 協力ゲームHanabiにおけるエージェント間の協調行動の分析 Estimation of Own State by Opponent’s Behavior in Cooperative Game Hanabi. 人工知能学会全国大会論文集 29: 1–4. Retrieved from http://ci.nii.ac.jp/naid/40020492009/en/

他者の意図を読み取る社会的知能タスクの中で最も困難なタスクの1つは、他者の行動から自身の状態を推定することです。この行動は人間の知能の特徴の一つであると言って良いでしょう。我々は他社の心を推定する人工知能タスクとして、Hanabiと呼ばれる協力ゲームを用いて研究をしています。Hanabiは、すべてのエージェントが協力して得点を集める協力型のカードゲームです。カード1〜5の列に代表される5色の花火を全てのプレイヤーが協力して組み立て、花火の大きさが得点になります。このゲームでは、プレイヤーは自分のカードを見ることはできませんが、自分のカード以外のすべてのエージェントのカードを見ることができます。ゲームでは、エージェント間の通信が制限されおり、各エージェントは他のプレーヤのカードの数や色を教えるために情報伝送のための資源を消費しなければいけません。

論文

  • Kawagoe, Atsushi, and Hirotaka Osawa. 2020. “Concordance of Risk Tendency Increases User’s Satisfaction: Verification of Agents in Cooperative Game Hanabi.” In International Conference on Human-Agent Interaction, 140–48.
  • Sato, Eisuke, and Hirotaka Osawa. 2019. “Reducing Partner’s Cognitive Load by Estimating the Level of Understanding in the Cooperative Game Hanabi.” In Advances in Computer Games, 1–12.
  • Kato, Takuya, and Hirotaka Osawa. 2018. “I Know You Better Than You Know Yourself: Estimation of Blind Self Improves Acceptance for an Agent.” In Proceedings of the 6th International Conference on Human-Agent Interaction - HAI ’18, 144–52. New York, New York, USA: ACM Press. https://doi.org/10.1145/3284432.3284453.
  • Hirotaka Osawa. 2015. Solving Hanabi : Estimating Hands by Opponent’s Actions in Cooperative Game with Incomplete Information. In AAAI workshop: Computer Poker and Imperfect Information, 37–43.
  • 大澤博隆. 2015. 協力ゲームHanabiにおけるエージェント間の協調行動の分析 Estimation of Own State by Opponent’s Behavior in Cooperative Game Hanabi. 人工知能学会全国大会論文集 29: 1–4. Retrieved from http://ci.nii.ac.jp/naid/40020492009/en/

解説