2013/02/27 by Fahiem Bacchus, Adam J. Grove, Bacchus, Fahiem +5
Computer Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Statistical Mechanics and Entropy #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.1302.6783
openalex publication_date 2013/02/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.