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人工智能 确定性 VS 不确定性

游戏AI技术一般有两种特性：确定性与不确定性。

确定性

确定性的行为或表现是指定好的可预言的。没有不确定的因素。简单的追逐算法就是确定性行为的一个例子。你可以明确的写一段代码控制电脑人物沿着X和Y坐标轴向到事先指定的目标点移动，直到人物的X和Y坐标与目标位置相同。

不确定性

不确定性是确定性相对的一种行为。这种行为有一定的不确定性和不可预知性。不确定行行为（不确定性的程度取决于怎样使用人工智能的方法和对这种方法的理解程度）的一个例子就是怎么使一个电脑人物学习怎么适应一个游戏玩家的战术。这种学习可以使用人工智能网络，贝叶斯定理，或者遗传算法。

确定性AI技术是游戏AI的基础。这些技术都是可预知，运行速度快，而且容易执行，理解，测试和调试。尽管确定性AI技术有很多优点让我们去使用他们，但也给开发人员的肩上放了很沉的包袱，要预测所有关卡的情节，编写所有的精确的行为。另外确定性方法没有能力学习和进化。而且游戏在玩了一些时间后，确定性行为就会被预见。可以这么说，这限制了一个游戏生命。

不确定性方法有学习能力而且有不可预见的游戏。进而游戏开发者不需要为关卡的所有行为精确的编写代码。不确定性方法可以自己学习和推断，他们的这种进化被称作紧急行为，或者在没有明确的指示下行动。 我们在书后讨论的棉屑和神经网络算法就是紧急行为很好的例子。

【原文】
Deterministic Versus Nondeterministic AI
Game AI techniques generally come in two flavors: deterministic and nondeterministic.

Deterministic

Deterministic behavior or performance is specified and predictable. There's no uncertainty. An example of deterministic behavior is a simple chasing algorithm. You can explicitly code a nonplayer character to move toward some target point by advancing along the x and y coordinate axes until the character's x and y coordinates coincide with the target location.




Nondeterministic

Nondeterministic behavior is the opposite of deterministic behavior. Behavior has a degree of uncertainty and is somewhat unpredictable (the degree of uncertainty depends on the AI method employed and how well that method is understood). An example of nondeterministic behavior is a nonplayer character learning to adapt to the fighting tactics of a player. Such learning could use a neural network, a Bayesian technique, or a genetic algorithm.

Deterministic AI techniques are the bread and butter of game AI. These techniques are predictable, fast, and easy to implement, understand, test, and debug. Although they have a lot going for them, deterministic methods place the burden of anticipating all scenarios and coding all behavior explicitly on the developers' shoulders. Further, deterministic methods do not facilitate learning or evolving. And after a little gameplay, deterministic behaviors tend to become predictable. This limits a game's play-life, so to speak.

Nondeterministic methods facilitate learning and unpredictable gameplay. Further, developers don't have to explicitly code all behaviors in anticipation of all possible scenarios. Nondeterministic methods also can learn and extrapolate on their own, and they can promote so-called emergent behavior, or behavior that emerges without explicit instructions. The flocking and neural network algorithms we'll consider in this book are good examples of emergent behavior.