Ore-Age: an Intelligent Tutoring System Model for Mining Method Selection

Abstract

Mining method selection is a critical decision for an economic, safe productive mining work. Each orebody is unique with its own properties and engineering judgment has a great effect on the decisions. In this study an intelligent assisting and tutoring system for preliminary underground mining method selection is developed. This systems is called Ore-Age, whose goal is making the preliminary mining method selection as effeciently as posssible too while giving them a remarkable education on mining method selection. Due to its semi-autonomous character, Ore-Age determşnes to direct its execution strategy based on the expertise levels od the users. Ore-Age acts as an assisting tool for the experienced engineers during the selection process and continuously looks for the help of its neuro-fuzzy learning algorithm. The reason of Ore-Age to take this learning procedure is to imitate and behave like these experts during his future selections. Besides this ability, Ore-Age tries to act as a tutoring system as efficiently as possible when he faces inexperienced engineers. The regular strategy of teaching process depends on an iterative algorithm checking the decisive concepts one by one to find the point of misconception leading to wrong selection. Furthermore as an alternative strategy, by his error modeling property, Ore-Age can alter hhis strategy and concentrate the users directly to the possible points of misconception, without using the previous "time demanding" algorithm. This system aiming the model thecognitive behaviour of the student is an indication of the reactive characteristic of the system that can alter the strategies based on his own logical decison to achieve a more efficient tutoring procedure. The system that is being developed in this study can be introduced as the first example of dynamic, intelligent assisting and tutoring systems in the mining profession.