December 2016
Intermediate to advanced
440 pages
9h 44m
Japanese
Content preview from アルゴリズムクイックリファレンス 第2版
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200
■
7
章
AI
における経路探索
大の子を選ぶのと最小の子を選ぶのを交互に行う。最後の点数の
3
は、元のプレイ
ヤーが、相手に対して、
3
と評価するゲーム状態を強制できることを示す。
図7-3 ミニマックスのゲーム木の例
7.3.1
入出力
ミニマックス
は、読みの深さ（
ply depth
）と呼ばれる固定数だけ手を先読みする。
ミニマックス
は、有効な手の中から、評価関数によって定まる、プレイヤーにとっ
て将来最良のゲーム状態を導くような手を返す。
7.3.2
文脈
ゲーム状態の評価は複雑であり、よりよいゲーム状態を決定するには、ヒューリ
スティックな評価に頼るしかない。実際に、チェス、チェッカー、リバーシ（オセ
ロの別名）などのゲームで有効な評価関数を開発するのは、知的なプログラムを設
計する上で最大の挑戦的課題となっている。ここでは、評価関数が入手可能と仮定
する。
ゲーム木のサイズは、各ゲーム状態での可能な手の数
b
で決まる。ほとんどのゲー
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.