Comparative evaluation of alternative induction engines forFeature

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 signiicantly 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.