Understanding master's thinking mechanism and developing its visualized system

2018 Fiscal Year Final Research Report

• Project/Area Number: 17K19968
• Research Category: Grant-in-Aid for Challenging Research (Exploratory)
• Allocation Type: Multi-year Fund
• Research Field: Information science, computer engineering, and related fields
• Research Institution: Japan Advanced Institute of Science and Technology
• Principal Investigator: IIDA HIROYUKI 北陸先端科学技術大学院大学, 先端科学技術研究科, 教授 (80281723)
• Research Collaborator: Jaap van den Herik Leiden University ; Yusof Umi Kalsom University of Science Malaysia ; Ismail Hadzariah Universiti Malaysia Sabah ; Aziz Norshakirah Universiti Teknologi PETRONAS ; Li Wenlin Huazhong University of Science and Technology
• Project Period (FY): 2017-06-30 – 2019-03-31
• Keywords: ゲーム木探索 / 証明数 / 共謀数 / シングル共謀数 / 大局観 / 思考の可視化

Outline of Final Research Achievements

Single Conspiracy Number (SCN) is a variant concept of conspiracy number and proof number which indicates the difficulty of a root node changing its MIN/MAX value to a certain score. It makes up the drawbacks of conspiracy number on computing complexity, and can be easily applied into different search frameworks. This study explores the potential usage of SCN as a long-term position evaluation to understand in-depth game progress patterns. Board game is chosen as a testbed, whereas a strong AI is used. It is implemented with alpha-beta search and modified to produce SCNs during the search process. Experiments are conducted on different types of positions including tactical positions and opening positions. The experimental results show that SCN is more consistent and accurate for long-term position evaluation than the conventional way using evaluation function values only, and using SCN together with evaluation function values enables us to better understand game progress patterns.

Free Research Field

ゲーム情報学

Academic Significance and Societal Importance of the Research Achievements

現在局面のミニマックス値がある評価値に更新される可能性が低いことは解の安定性を意味する．解の安定性の指標を見つけることはより優れた解，つまり最善手を見つける上で重要不可欠である．名人の大局観はまさに解の安定性を精密に判断する能力と言える．この意味で，本研究は人工知能の観点から名人の大局観の本質を明らかにした．ただし，名人が解の安定性の指標である共謀数またはシングル共謀数をどのように計算しているのかは今後の課題である．本研究により，試合パターンの長期視野での理解が可能となり，特に，不利な状況に陥ることが予想される場合，早めのリスク管理として対処可能となるので，一般分野への幅広い応用が期待される．