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

Abstract

In the past studies about the mining method selection process, which is among the most critical aspects in the mining engineering discipline, there are attempts to build up a systematic approach to make this selection. But, these approaches work based on static databases and fail in inserting the intuitive feelings and engineering judgments of experienced engineers to the selection process. In this study, a hybrid system based on 13 different expert systems and one interface agent is developed, to make mining method selection for the given ore-bodies. The learning procedure to insert the expertise of the experienced engineers to the selection process, works based on a neuro-fuzzy model, combining the TSK model of the fuzzy theory and a two layered neural network with the utilization of the back-propagation algorithm. Again, to supply the maximum assistance to the users, the agent executes the system's tutoring procedure in case an inexperienced user enters the system, to complete his/her missing knowledge about mining method selection. 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.