Discovery of Dependencies and Models
Machine discovery systems help humans to find natural laws from collections of experimentaUy collected data. Most of the laws found by existing machine discovery systems describe static situations, where a physical system has reached equilibrium. In this paper, we consider the problem of discovering laws that govern the behavior of dynamic systems, i.e., systems that change their state over time. Based on ideas from inductive logic programming and machine discovery, we present two systems for discovery of qualitative and quantitative laws from quantitative (numerical) descriptions of dynamic system behavior.