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

人工智能（AI）领域的目标是 开发能够感知、推理、交流和 在复杂的环境中行动，就像人类一样， 比人类做得更好 尽管这个领域已经产生了一些 实际上有用的机器与这些能力的雏形，它 人们普遍承认，最终目标仍然遥远。 既然如此，关于什么是 人工智能的最佳方法是 实现最终目标的基础，以及 产生实际有用的短期结果的意义。 因此，在本发明中， 过去出现了许多不同的范例 三十五年左右。 每一种都有其热心的拥护者， 已经产生了足够多的有趣的结果， 可以立即解雇 也许这些方法的组合 将被要求。 无论如何，这些方法的倡导者 我常常觉得他们的方法论是一种“突破性的”方法论， 值得特别支持。为了让研究人员了解情况， 其他人与这些范式和他们的主要结果，圣诞老人 Fe研究所组织了一个研讨会，积极倡导 将邀请采用不同的方法进行工作。 这样的 车间允许领导工人了解彼此的观点 以及它们之间的关系。 特别是 研讨会并不是围绕着经典的 人工智能的主要子学科，即机器视觉、自然语言 处理，知识表示，规划，专家系统，和 等等。相反，不同的观点是如何提出的 AI的整体或主要部分应该受到攻击。

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.