| V I S U A L I Z Z A D I S C U S S I O N E |
| gigietto |
Inserito il - 21/02/2017 : 15:58:29
Academic AI vs. Game AI There is an important distinction to be made between the AI studied by academics and that used in computer games. Academic research is split into two camps: strong AI and weak AI. The field of strong AI concerns itself with trying to create systems that mimic human thought processes and the field of weak AI (more popular nowadays) with applying AI technologies to the solution of real-world problems. However, both of these fields tend to focus on solving a problem optimally, with less emphasis on hardware or time limitations. For example, some AI researchers are perfectly happy to leave a simulation running for hours, days, or even weeks on their 1000-processor Beowolf cluster so long as it has a happy ending they can write a paper about. This of course is an extreme case, but you get mypoint. Game AI programmers, on the other hand, have to work with limited resources. The amount of processor cycles and memory available varies from platform to platform but more often than not the AI guy will be left, like Oliver holding out his bowl, begging for more. The upshot of this is that compromises often have to be made in order to get an acceptable level of performance. In addition, successful games — the ones making all the money —doonething very well: They entertain the player (or they have a film license ). Ipso facto, the AI must be entertaining, and to achieve this must more often than not be designed to be suboptimal. After all, most players will quickly become frustrated and despondent with an AI that always gives them a whippin’. To be enjoyable, an AI must put up a good fight but lose more often than win. It must make the player feel clever, sly, cunning, and powerful. It must make the player jump from his seat shouting, “Take that, you little shit!” |
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