1.3 The Future of Game AI
游戏AI的下一个大进步就是学习。不是让所有的电脑人物的行为被事先设定好，游戏应该发展，学习，而当游戏被玩的越多越适应。游戏同玩家同时进步的结果是，玩家更难预测游戏的结果，这样就会延长游戏的游戏周期。正好这些不确定技术的学习和发展，给那些传统的游戏AI开发者解决学习技术打了一针兴奋剂。
学习和与人物行为互动的技术属于，我们在前面讨论的不确定AI技术，在这儿他非常难实现。尤其像未确定，学习AI技术需要更长时间的开发和测试。而且更难真正理解游戏AI正在做什么，这样就更难调试了。这些因素都是阻碍游戏AI技术更广发展的原因。然而，一切正在改变。
很多主流的游戏像，Creatures, Black & White, Battlecruiser 3000AD, Dirt Track Racing, Fields of Battle, and Heavy Gear 都使用了未确定游戏AI的方法。这些游戏的成功从新燃起了对游戏AI方法的热情，比如：决定树，神经元网络，遗传算法，和盖然论。
这些成功的游戏都是使用不确定方法和更多的传统的确定性方法相结合，并且是只有真正的需要且适合的时候才使用。神经元网络不是灵丹妙药可以解决所有的问题；但是，你可以在混合的AI系统里使用它来解决更特别的AI功能，这样会得到很好的效果。这是我们提倡使用不确定方法的使用方法。这样，你至少可以将你的AI系统分开，一部分是难开发，测试和调试的不确定方法，而你的主要的AI系统还是保持传统的方式。
整个这本书涵盖了传统的游戏AI技术和相关的新技术，未来的AI技术。这本书的目的就是让你完全明白游戏的AI做了什么，和要继续做什么。同样也希望你了解几个比较有前途的新技术，为你将来开发下一代游戏AI开个头。
【原文】1.3 The Future of Game AI The next big thing in game AI is learning. Rather than have all nonplayer character behavior be predestined by the time a game ships, the game should evolve, learn, and adapt the more it's played. This results in a game that grows with the player and is harder for the player to predict, thus extending the play-life of the game. It is precisely this unpredictable nature of learning and evolving games that has traditionally made AI developers approach learning techniques with a healthy dose of trepidation. The techniques for learning and reacting to character behavior fall under the nondeterministic AI we talked about earlier, and its difficulties apply here too. Specifically, such nondeterministic, learning AI techniques take longer to develop and test. Further, it's more difficult to really understand what the AI is doing, which makes debugging more difficult. These factors have proven to be serious barriers for widespread use of learning AI techniques. All this is changing, though. Several mainstream games, such as Creatures, Black & White, Battlecruiser 3000AD, Dirt Track Racing, Fields of Battle, and Heavy Gear, used nondeterministic AI methods. Their success sparked a renewed interest in learning AI methods such as decision trees, neural networks, genetic algorithms, and probabilistic methods. These successful games use nondeterministic methods in conjunction with more traditional deterministic methods, and use them only where they are needed and only for problems for which they are best suited. A neural network is not a magic pill that will solve all AI problems in a game; however, you can use it with impressive results for very specific AI tasks within a hybrid AI system. This is the approach we advocate for using these nondeterministic methods. In this way, you can at least isolate the parts of your AI that are unpredictable and more difficult to develop, test, and debug, while ideally keeping the majority of your AI system in traditional form. Throughout this book we cover both traditional game AI techniques as well as relatively new, up-and-coming AI techniques. We want to arm you with a thorough understanding of what has worked and continues to work for game AI. We also want you to learn several promising new techniques to give you a head start toward the future of game AI.
