从旧信念中产生新信念
Generating New Beliefs From Old
摘要：
在以前的工作[BGHK 92，BGHK 93]中，我们研究了随机世界方法-一种用于生成置信度的特殊（并且非常强大）方法（即，主观概率）从由客观（一阶、统计和缺省）信息组成的知识库中。但是，允许知识库只包含客观信息有时是有限的。我们偶尔也希望在知识库中包含有关信念程度的信息，因为在某些情况下，旧信念代表了影响新信念的重要信息。在本文中，我们描述了三个相当一般的技术，扩展的方法，产生程度的信念，从客观的信息，可以利用程度的信念。我们所有的技术都是基于众所周知的方法，如交叉熵。我们讨论的技术之间的一般连接，特别是表明，虽然在概念上和技术上有很大的不同，所有的技术都给出了相同的答案时，适用于随机世界的方法。
英文摘要：
In previous work [BGHK92, BGHK93], we have studied the random-worlds approach -- a particular (and quite powerful) method for generating degrees of belief (i.e., subjective probabilities) from a knowledge base consisting of objective (first-order, statistical, and default) information. But allowing a knowledge base to contain only objective information is sometimes limiting. We occasionally wish to include information about degrees of belief in the knowledge base as well, because there are contexts in which old beliefs represent important information that should influence new beliefs. In this paper, we describe three quite general techniques for extending a method that generates degrees of belief from objective information to one that can make use of degrees of belief as well. All of our techniques are bloused on well-known approaches, such as cross-entropy. We discuss general connections between the techniques and in particular show that, although conceptually and technically quite different, all of the techniques give the same answer when applied to the random-worlds method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
