Belief Revision in a Discrete Temporal Probability-Logic

We describe a discrete time probabilitylogic for use as the representation language of a temporal knowledge base. In addition to the usual expressive power of a discrete temporal logic, our language allows for the speci cation of non-universal generalizations in the form of statistical assertions. This is similar to the probability-logic of Bacchus, but di ers in the inference mechanisms. In particular, we discuss two interesting and related forms of inductive inference: interpolation and extrapolation. Interpolation involves inferences about a time interval or point contained within an interval for which we have relevant statistical information. Extrapolation extends statistical knowledge beyond the interval to which it pertains. These inferences can be studied within a static temporal knowledge base, but the further complexity of dynamically accounting for new observations makes matters even more interesting. This problem can be viewed as one of belief revision in that new observati...

• Type: Conference
• Year: 1994
• Where: TIME
• Authors: Scott D. Goodwin, Howard J. Hamilton, Eric Neufeld, Abdul Sattar, André Trudel
• Keywords: Automated Reasoning, Belief Revision, Discrete Temporal Logic, Temporal Knowledge Base, TIME 1994