游戏开发中的人工智能

原文摘录   · · · · · · 

• 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. At least until recently, another factor that has limited game AI development is the fact that developers have been focusing most of their attention on graphics quality. As it turns out, such focus on developing better and faster graphics techniques, including hardware acceleration, might now afford more resources to be allocated t... (查看原文)

  Liagc 2019-06-30 20:02:58

• Developers commonly use fuzzy logic in fuzzy state machines to make the resulting actions somewhat less predictable and to reduce the burden of having to enumerate huge numbers of if-then rules. 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. (查看原文)

  Liagc 2019-06-30 20:02:58