Comparative evaluation of alternative induction engines for Feature Based Modelling
Feature Based Modelling has demonstrated the ability to produce
agent models with high accuracy in predicting an agent's future actions.
There are a number of respects in which this modelling technique is novel.
However, there has been no previous analysis of which aspects of the
approach are responsible for its performance. One distinctive feature of the
approach is a purpose built induction module. This paper presents a study
in which the original custom built Feature Based Modelling induction
module was replaced by the C4.5 machine learning system. Comparative
evaluation shows that the use of C4.5 increases the number of predictions
made without significantly altering the accuracy of those predictions. This
suggests that it is the general input-output agent modelling methodology
used with both systems that has primary responsibility for the high
predictive accuracy previously reported for Feature Based Modelling,
rather than its initial idiosyncratic induction technique.