HIERARCHAL INDUCTIVE PROCESS MODELING AND ANALYSIS ...

models of natural systems are non-unique, there is multiple ways to represent the
same dynamic. Creating computational tools that would quickly and automatically
evaluate multiple models seemed to be a promising idea to search through the extensive
model space. The success of machine learning and data mining in commercial
domains led scientists to investigate the field of automated modeling to serve that
particular purpose (Fayyad et al., 1996).
The act of gathering small pieces of information and combining it to prior knowledge
to formulate a complex overview of an object or process studied is called induction.
Induction prevents from searching the entire space of possible equations
by only piecing together the meaningful terms, for instance a predator-prey model
will need terms specifying growth and death (Todorovski et al. 2005). Inductive
modeling methods (i.e. LAGRAMGE, HIPM, ARIMA, FUSE) use the principles of
induction to construct models of the studied system. Methods used for commercial
application, such as Knowledge Discovery in Database (KDD) process, were insufficient
for scientific purposes as they only described and did not explain the observed
system behavior (Langley et al. 2006). A simple example would be the modeling of
water consumption in a city, a water company could easily create a numerical model
based on previous years that would give a good estimate of the projected water
consumption over time but it may not explain why the consumption fluctuates the
way it does. In other words the commercial methods were able to produce models
that are useful when trying to make accurate predictions for a system but become
very limited when trying to explain which processes drive systems behaviors; these
methods did not explore the realm of all possible models. Thus, induction methods
had to be enhanced to automate the task of building and evaluating multiple models
(Dzeroski et al. 1995).
In this thesis, I used the hierarchal inductive process modeling technique, which
is encoded as computer algorithm called HIPM (Langley et al. 2006; Bridewell et
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