U.S. military forces require personnel who are prepared for new missions, who are equipped to face ever-changing operating environments, and who are proficient in increasingly sophisticated, rapidly evolving technologies. To successfully support personnel, the training, operational, and personnel communities must be aware of the rapidly changing needs of those in the field and understand how decisions concerning the allocation of limited recruiting and training resources may affect their readiness.

This paper describes how competency definitions can support resource allocation decision-making by linking data on experience in the field with personnel and training data. Linking these data automatically allows training managers to quantitatively compare how tasks are trained in the schools with how tasks are executed in the field and to adjust training time and equipment resources accordingly. Competency descriptions with multiple levels of abstraction can be used to summarize data at the level appropriate for the decision-maker.

Complex competency definitions can be expensive to build and difficult to update. An automated approach to generating competency definitions that leverages standard reusable competency definition data models and existing taxonomies can reduce the development effort and speed up maintenance.

This paper provides an example where competency definitions are generated automatically using an ontology. The model has been used to integrate operational data on equipment used in the current operating environment, personnel data on driving accidents, and training data on equipment used for training. It allows decision-makers to compare operational risk in terms of the cost of accidents involving particular types of vehicles in the field with the investment in driver training time by type of vehicle at U.S. Army schools.