. Machine discovery systems help humans to find natural laws from collections of experimentally 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 dynamical systems, i.e., systems that change their state over time. Based on ideas from inductive logic programming and machine discovery, we present two systems, QMN and LAGRANGE, for discovery of qualitative and quantitative laws from quantitative (numerical) descriptions of dynamical system behavior. We illustrate their use by generating a variety of dynamicalsystem models from examplebehaviors. Keywords: machine discovery, machine learning, dynamical system identification 1. Introduction The task of modeling natural and artificial dynamical systems, i.e., systems that change their state over time, is omnipresent. Usually, dynamical systems are descr..