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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196
■
7
章
AI
における経路探索
べ替える。しかし、評価関数が有能でないと、ここで述べる経路探索アルゴリズム
は、最良の手を選ぶことができない。諺にもあるように、「ゴミを入れると、出るの
はゴミだけ」だ。
静的評価関数は、ゲーム木の位置のさまざまの特性を考慮に入れた上で、プレイ
ヤーの観点から、その位置の相対的強さを反映した点数を返さなければならない。
例えば、サミュエルが開発した、チェッカーのゲームで最初に成功したプログラム
（
Arthur Samuel 1959
）では、「駒数特性」（プレイヤーと相手との駒数の比較）や「交
換特性」（勝つときに仕掛ける駒の交換、負けるときにはしない）など
24
のゲーム特
性を用いて盤面を評価していた。明らかに、より正確な評価関数はゲーム解決エン
ジンをより優れたプレイヤーにする。
本章では、三目並べにニルソンが定義した点数計算関数
BoardEvaluation
を用い
る（
Nils Nilsson, 1971
）。
nc(gs, p)
をゲーム状態
gs
でプレイヤー
p
が三目並べるこ
とができる行、列、対角線の個数とする。そこで、
score (gs, p)
を次のように定義す
る。
●
プレイヤー
p
がゲーム状態
gs
でゲームに勝つなら＋∞
●
プレイヤー
p
の相手がゲーム状態
gs
でゲームに勝つなる−∞
●
どちらのプレイヤーもゲーム状態
gs
でゲームに勝たないなら
nc(gs, p)
−
nc(gs, opponent)
7.2
経路探索の概念
次の概念は、
2
人ゲーム木と
1
人プレイヤーの探索木
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.