少数の記録からプレイヤの価値観を機械学習するチームプレイAIの構成  [in Japanese] Design of a Teammate AI by Learning Human-player Utility from a few Records of Actions  [in Japanese]

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Abstract

市販のコンピュータゲーム特に RPG と呼ばれるジャンルでは,ゲーム AI が操作するキャラクタとチームを組んで遊べるものも多いが,しばしば仲間 AI プレイヤは期待に反する行動を取り,プレイヤの不満に繋がる.これはこの種のゲームに "勝つ" 以外の副目的が複数あり,AI プレイヤは人間プレイヤの "どの目的をどの程度重視しているか" といった価値観を理解せずに行動していることが原因の一つである.本研究では,人間プレイヤが選択した行動から人間プレイヤの重視する目的を推定し,それを AI プレイヤの行動選択に活用することでその人間プレイヤにとって満足度が高い AI プレイヤを生成することを目指す.評価実験では,様々な価値観を持つ仮想人間プレイヤを人工的に構成し,提案手法を適用して価値観を推定した.全く同じ価値観に基づいて行動を選択した場合の行動一致率 (例えば 70.6%) に対し,推定した価値観に基づいて行動を選択した場合の行動一致率 (例えば 67.1%) は,最悪の場合でも 3.5% しか劣っていない結果を得ることができた.: Some genres of commercial video games, especially RPG games, allow players to play the game with the AI players as the teammates. But the AI players as the teammates often take actions that the human player does not expect them to do. Such mismatches between the expectations of the human players and the actions taken by the AI players often cause dissatisfaction of the players. One of the reasons for such mismatches is that there are several types of sub-goals in these games and the AI players act without understanding which types of sub-goals are important for each human player. The purpose of this study is to propose a method to develop teammate AI players that estimate the sub-goal preference of the human players and act with causing less dissatisfaction of the players. In an evaluation experiment, we prepared some artificial players with various preferences for the sub-goals and tried to estimate their sub-goals by the proposed method. The selected actions based on the estimated sub-goal preferences were the same as the selected actions by the original artificial players at the rate of 67.1% in one setting. The upper bound of the rate is about 70.6% (in this setting), which is the rate at which the same actions are selected when the preference of sub-goals is the same. Thus the proposed method is only 3.5% inferior in performance in the worst case compared to an ideal estimation.

Some genres of commercial video games, especially RPG games, allow players to play the game with the AI players as the teammates. But the AI players as the teammates often take actions that the human player does not expect them to do. Such mismatches between the expectations of the human players and the actions taken by the AI players often cause dissatisfaction of the players. One of the reasons for such mismatches is that there are several types of sub-goals in these games and the AI players act without understanding which types of sub-goals are important for each human player. The purpose of this study is to propose a method to develop teammate AI players that estimate the sub-goal preference of the human players and act with causing less dissatisfaction of the players. In an evaluation experiment, we prepared some artificial players with various preferences for the sub-goals and tried to estimate their sub-goals by the proposed method. The selected actions based on the estimated sub-goal preferences were the same as the selected actions by the original artificial players at the rate of 67.1% in one setting. The upper bound of the rate is about 70.6% (in this setting), which is the rate at which the same actions are selected when the preference of sub-goals is the same. Thus the proposed method is only 3.5% inferior in performance in the worst case compared to an ideal estimation.

Journal

  • 研究報告ゲーム情報学(GI)

    研究報告ゲーム情報学(GI) 2015-GI-33(5), 1-8, 2015-02-26

    Information Processing Society of Japan (IPSJ)

Codes

  • NII Article ID (NAID)
    110009882472
  • NII NACSIS-CAT ID (NCID)
    AA11362144
  • Text Lang
    JPN
  • Article Type
    journal article
  • ISSN
    09196072
  • Data Source
    NII-ELS  IR 
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