Generating New Beliefs From Old
This work addresses a specific problem in AI and knowledge representation for researchers and practitioners dealing with belief generation, but it is incremental as it builds on established approaches like cross-entropy.
The paper tackles the limitation of generating degrees of belief solely from objective information by introducing three techniques to incorporate existing beliefs into the knowledge base, showing that all techniques yield the same result when applied to the random-worlds method.
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.