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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/13464

Title: 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
Authors: 和田, 堯之
佐藤, 直之
池田, 心
Keywords: ロールプレイングゲーム(RPG)
チームプレイ
機械学習
価値観
効用関数
Issue Date: 2015-02-26
Publisher: 情報処理学会
Magazine name: 研究報告ゲーム情報学(GI)
Volume: 2015-GI-33
Number: 5
Start page: 1
End page: 8
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.
Rights: 社団法人 情報処理学会, 和田 堯之, 佐藤 直之, 池田 心, 研究報告ゲーム情報学(GI), 2015-GI-33(5), 2015, 1-8. ここに掲載した著作物の利用に関する注意: 本著作物の著作権は(社)情報処理学会に帰属します。本著作物は著作権者である情報処理学会の許可のもとに掲載するものです。ご利用に当たっては「著作権法」ならびに「情報処理学会倫理綱領」に従うことをお願いいたします。 Notice for the use of this material: The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). This material is published on this web site with the agreement of the author (s) and the IPSJ. Please be complied with Copyright Law of Japan and the Code of Ethics of the IPSJ if any users wish to reproduce, make derivative work, distribute or make available to the public any part or whole thereof. All Rights Reserved, Copyright (C) Information Processing Society of Japan.
URI: http://hdl.handle.net/10119/13464
Material Type: publisher
Appears in Collections:b10-1. 雑誌掲載論文 (Journal Articles)

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