Inproceedings,
Finding Semi-Quantitative Physical Models Using Genetic Programming
The 6th annual UK Workshop on Computational Intelligence, page 245--252. Leeds, UK, (4-6 September 2006)
Abstract
Model learning often implies exploring a vast search
space of possible hypotheses in the hope of finding a
solution. Qualitative model learners are mostly based
on Inductive Logic Programming (ILP), which is a
systematic method which tends to be well fitted for
exploring solutions in a narrow search space. We
present a semi-quantitative model learner that uses
Genetic Programming (GP), which is well suited for
exploring a broad search space. We learn simple
physical systems based on a formalism involving both
crisp numbers and fuzzy quantity spaces. We use the ECJ
framework,1 and the fitness of a model is set to be
optimal when it covers all positive examples. Several
experiments are performed to learn and reuse models of
physical systems of increasing complexity; firstly a
u-tube, then coupled tanks, and finally cascading
tanks. Results show that the system can approximate the
target models in reasonably good conditions, and that
there is still scope for optimisation.
