From Statistics to Beliefs

An intelligent agent uses known facts, including statistical knowledge, to assign degrees of belief to assertions it is uncertain about. We investigate three principled techniques for doing this. All three are applications of the principle of indi erence, because they assign equal degree of belief to all basic \situations" consistent with the knowledge base. They di er because there are competing intuitions about what the basic situations are. Various natural patterns of reasoning, such as the preference for the most speci c statistical data available, turn out to follow from some or all of the techniques. This is an improvement over earlier theories, such as work on direct inference and reference classes, which arbitrarily postulate these patterns without o ering any deeper explanations or guarantees of consistency. The three methods we investigate have surprising characterizations: there are connections to the principle of maximum entropy, a principle of maximal independence, a...

• Type: Conference
• Year: 1992
• Where: AAAI
• Authors: Fahiem Bacchus, Adam J. Grove, Daphne Koller, Joseph Y. Halpern
• Keywords: AAAI 1992, Intelligent Agents, Knowledge Base, Statistical Knowledge, Various Natural Patterns