迈向人工智能的步骤

M. Minsky
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引用次数: 1408

摘要

启发式编程的问题——让计算机解决真正困难的问题——分为五个主要领域:搜索、模式识别、学习、计划和归纳。从某种意义上说,计算机只能做它被告知要做的事情。但是,即使当我们不知道如何解决某个问题时,我们也可以给机器(计算机)编程,让它在一些大的解决方案尝试空间中进行搜索。不幸的是,这通常会导致效率极低的过程。使用模式识别技术,通过限制机器方法在适当问题上的应用,通常可以提高效率。模式识别与学习相结合,可以在积累经验的基础上利用归纳,进一步减少搜索。通过分析情况,使用Planning方法,我们可以用更小、更合适的探索取代给定的搜索,从而获得根本性的改进。为了处理各种各样的问题,机器将需要使用一些归纳法来构建它们的环境模型。在适当的情况下,通过大量引用文献和对迄今为止构建的一些最成功的启发式(解决问题)程序的描述来支持讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Steps toward Artificial Intelligence
The problems of heuristic programming-of making computers solve really difficult problems-are divided into five main areas: Search, Pattern-Recognition, Learning, Planning, and Induction. A computer can do, in a sense, only what it is told to do. But even when we do not know how to solve a certain problem, we may program a machine (computer) to Search through some large space of solution attempts. Unfortunately, this usually leads to an enormously inefficient process. With Pattern-Recognition techniques, efficiency can often be improved, by restricting the application of the machine's methods to appropriate problems. Pattern-Recognition, together with Learning, can be used to exploit generalizations based on accumulated experience, further reducing search. By analyzing the situation, using Planning methods, we may obtain a fundamental improvement by replacing the given search with a much smaller, more appropriate exploration. To manage broad classes of problems, machines will need to construct models of their environments, using some scheme for Induction. Wherever appropriate, the discussion is supported by extensive citation of the literature and by descriptions of a few of the most successful heuristic (problem-solving) programs constructed to date.
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