A scenario generation framework for automating instructional support in scenario-based training

Abstract

Within training, scenario creation can be a long and costly activity. This often results in the same scenarios being re-used. While this can work with new trainees, it does not provide effective training for those using a system for continuing training. In order to provide an easier capability for instruction, the authors are pursuing a line of research in scenario generation. While this includes methods for instructors to build scenarios easily via a manual process, automated approaches for scenario generation are also being investigated.The authors have previously completed efforts in reviewing the needs of a scenario generation system (Martin et al., 2009) and in building a conceptual model for scenarios and how varying complexity can be achieved. This paper provides a review of this work and then presents a computational approach to automated scenario generation. The authors are pursuing research investigating processes and tools for scenario generation, both manual and automated.A recently re-discovered approach (Shape Grammars) and a newly-developed approach (Functional L-systems) each shows great promise for their use in automated scenario generation. However, based on their additional expressive power, the authors have chosen to use Functional L-systems within their software, which will be reviewed.