Workshop on Approaches to Artificial Intelligence, Santa Fe,New Mexico, November 1992

人工智能（AI）领域的目标是开发能够像人类一样在复杂环境中感知、推理、交流和行动的机器，甚至可能比人类做得更好。尽管该领域已经生产出一些具有这些基本能力的实用机器，但人们普遍认为，最终目标仍然遥远。因此，关于什么是为实现最终目标奠定核心基础的最佳人工智能方法，以及在产生实际有用的短期结果方面的最佳方法，存在许多讨论和争论。因此，在过去35年左右的时间里出现了许多不同的范例。每一种都有其狂热的拥护者，其中一些已经产生了足够多的有趣的结果，以至于不能马上被驳回。也许需要将这些方法结合起来。在任何情况下，这些方法的拥护者经常觉得他们的方法是值得特别支持的“突破性”方法。为了使研究人员和其他人熟悉这些范例及其主要成果，圣菲研究所组织了一个研讨会，邀请积极从事不同方法工作的倡导者参加。这样的研讨会可以让领导工人了解彼此的观点和他们之间的关系。具体来说，研讨会并没有围绕人工智能的主要分支学科，即机器视觉、自然语言处理、知识表示、规划、专家系统等进行组织。相反，关于如何攻击AI的整体或主要部分，他们提出了不同的观点。

The field of artificial intelligence (AI) has as its goal the development of machines that can perceive, reason, communicate, and act in complex environments much like humans can, or possibly even better than humans can. Even though the field has produced some practically useful machines with rudiments of these abilities, it is generally conceded that the ultimate goal is still distant. That being so, there is much discussion and argument about what are the best approaches for AI in the sense of laying the core foundations for achieving ultimate goals as well as best in the sense of producing practically useful shorter-term results. Thus, a number of different paradigms have emerged over the past thirty-five years or so. Each has its ardent advocates, and some have produced sufficiently many interesting results so as not to be dismissable out of hand. Perhaps combinations of these approaches will be required. In any case, the advocates of these approaches often feel that theirs is the "breakthrough" methodology that deserves special support. In order to acquaint researchers and others with these paradigms and their principal results, the Santa Fe Institute organizes a workshop to which advocates actively working in the different approaches will be invited. Such a workshop allows leading workers to understand each others' points of view and the relationships among them. Specifically the workshop is not organized around what classically have been the main sub-disciplines of AI, namely machine vision, natural language processing, knowledge representation, planning, expert systems, and so on. Instead, different perspectives are presented on how the whole, or major parts, of AI ought to be attacked.