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ORE-AGE: AN INTELLIGENT TUTORING SYSTEM MODELFOR MINING METHOD SELECTION

BROWSE_DETAIL_CREATION_DATE: 2013

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BROWSE_DETAIL_TYPE: Article

BROWSE_DETAIL_PUBLISH_STATE: Published

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: TECHNOLOGY, Mining engineering. Metallurgy,

BROWSE_DETAIL_CREATORS: Güray, Cenk (Author),

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BROWSE_DETAIL_PUBLISHER: Atılım Üniversitesi BROWSE_DETAIL_PUBLICATION_NAME: The International Journal of Mineral Resources Engineering BROWSE_DETAIL_PUBLICATION_LOCATION: Ankara BROWSE_DETAIL_PUBLICATION_DATE: 2008 BROWSE_DETAIL_PUBLICATION_NUMBER: 1 BROWSE_DETAIL_PUBLICATION_VOLUME: 13 BROWSE_DETAIL_PUBLICATION_PAGE: 15-40


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BROWSE_DETAIL_TAB_ABSTRACT

Mining method selection is a critical decision for an economic, safe and 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 system is called Ore-Age, whose goal is making the preliminary mining method

selection as efficiently as possible and to make the users perform this selection as

efficiently as possible too while giving them a remarkable education on mining

method selection. Due to its semi-autonomous character, Ore-Age determines to

direct its execution strategy based on the expertise levels of the users. Ore-Age acts

as an assisting tool for the experienced engineers during the selection process and

continuously looks for chances to direct the experiences of such expert engineers to

his own database by 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 a wrong selection. Furthermore as

an alternative strategy, by his error modeling property, Ore-Age can alter his strategy

and concentrate the users directly to the possible points of misconception, without

using the previous “time demanding” algorithm. This system aiming the model the

cognitive behavior of the student is an indication of the reactive characteristic of the

system that can alter the strategies based on his own logical decision 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.


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