Towards a Method of Searching a Diverse Theory Space for Scientific Discovery

Scientists need customizable tools to help them with discovery. We present an adjustable heuristic function for scientific discovery. This function may be considered in either a Minimum Message Length (MML) or a Bayesian Net manner. The function is approximate because the default method of specifying theory prior probabilities is a gross estimate and because there is more to theory choice than maximizing probability. We do, however, effectively capture some user preferences with our technique. We show this for the qualitatively different domains of geophysics and sociology.

• Type: Conference
• Year: 2001
• Where: DIS
• Authors: Joseph Phillips
• Keywords: Adjustable Heuristic Function, Bayesian Net Manner, DIS 2001, Theoretical Computer Science, Theory Prior Probabilities