A hybrid expert system for scheduling the US Army's close combat tactical trainer

A hybrid expert system for scheduling the US Army's close combat tactical trainer

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Article ID: iaor20022355
Country: United States
Volume: 11
Issue: 2
Start Page Number: 157
End Page Number: 176
Publication Date: Jan 1996
Journal: Expert Systems with Applications
Authors: ,
Keywords: scheduling, artificial intelligence: expert systems
Abstract:

In recent years, the United States Army has undertaken the development of a new type of computerized training facility called the Close Combat Tactical Trainer (CCTT). This system will enable future armored and mechanized units at battalion and below to train in virtual environments on a digitized battlefield. The major tasks for planning a days' training include the following. First, personnel from the unit undergoing training select training scenarios to be conducted during each training day. Next, the training scenarios are scheduled throughout the planning horizon where multiple scenarios may be scheduled simultaneously. Finally, the type and quantity of training resources for conducting each training scenario are identified and scheduled. Resource quantities vary by training scenario type and may vary within a scenario type as well. CCTT training resources include simulator modules, computer workstations, workstation operators (people) and computer-generated, semi-automated forces (SAF). This paper discusses the development of a hybrid expert system prototype for scheduling training in the new CCTT facilities.

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