Journal of Intelligent Information Systems

, Volume 4, Issue 1, pp 89–108 | Cite as

Discovering dynamics: From inductive logic programming to machine discovery

  • Saso Dzeroski
  • Ljupco Todorovski


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 dynamical system models from example behaviors.


machine discovery machine learning dynamical system identification 


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Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Saso Dzeroski
    • 1
  • Ljupco Todorovski
    • 1
  1. 1.Institut Jozef StefanLjubljanaSlovenia

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