On the thresholds of knowledge
On the thresholds of knowledge
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DOI：
10.1109/aiia.1988.13308
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发表时间：
1987-08
期刊：
影响因子：
--
通讯作者：
D. Lenat;E. Feigenbaum
中科院分区：
文献类型：
--
作者：
D. Lenat;E. Feigenbaum
阐述了人工智能领域的三个主要发现。第一个是知识原则，它指出如果一个程序要很好地执行一项复杂的任务，它必须对它运行的世界有很多了解。第二个是该原则的合理延伸，称为广度假设，它指出在意外情况下智能行为还需要两种额外的能力：依靠日益普遍的知识，以及类比具体但广泛的知识。第三个发现是人工智能的概念作为一个经验查询系统，需要对大问题的想法进行实验测试。结论是，这些概念共同可以确定未来人工智能研究的方向。>
Three major findings in the domain of artificial intelligence are articulated. The first is the knowledge principle, which states that if a program is to perform a complex task well, it must know a great deal about the world in which it operates. The second is a plausible extension of that principle, called the breadth hypothesis, which states that there are two additional abilities necessary for intelligent behavior in unexpected situations: falling back on increasingly general knowledge, and analogizing to specific but far-flung knowledge. The third finding is a concept of AI as an empirical inquiry system requiring the experimental testing of ideas on large problems. It is concluded that together these concepts can determine a direction for future AI research.>