Training typically begins with a pre-existing scenario. The training exercise is performed and then an after action review is sometimes held. This “training pipeline” is repeated for each scenario that will be used that day. This approach is used routinely and often effectively, yet it has a number of aspects that can result in poor training. In particular, this process commonly has two associated events that are undesirable. First, scenarios are re-used over and over, which can reduce their effectiveness in training. Second, additional responsibility is placed on the individual training facilitator in that the trainer must now track performance improvements between scenarios. Taking both together can result in a multiplicative degradation in effectiveness. Within any simulation training exercise, a scenario definition is the starting point. While these are, unfortunately, re-used and over-used, they can, in fact, be generated from scratch each time. Typically, scenarios include the entire configuration for the simulators such as entities used, time of day, weather effects, entity starting locations and, where applicable, munitions effects. In addition, a background story (exercise briefing) is given to the trainees. The leader often then develops a mission plan that is shared with the trainee group. Given all of these issues, scientists began to explore more purposeful, targeted training. Rather than an ad-hoc creation of a simulation experience, there was an increased focus on the content of the experience and its effects on training. Previous work in scenario generation, interactive storytelling and computational approaches, while providing a good foundation, fall short on addressing the need for iv adaptive, automatic scenario generation. This dissertation addresses this need by building up a conceptual model to represent scenarios, mapping that conceptual model to a computational model, and then applying a newer procedural modeling technique, known as Functional L-systems, to create scenarios given a training objective, scenario complexity level desired, and sets of baseline and vignette scenario facets. A software package, known as PYTHAGORAS, was built and is presented that incorporates all these contributions into an actual tool for creating scenarios (both manual and automatic approaches are included). This package is then evaluated by subject matter experts in a scenario-based “Turing Test” of sorts where both system-generated scenarios and human-generated scenarios are evaluated by independent reviewers. The results are presented from various angles. Finally, a review of how such a tool can affect the training pipeline is included. In addition, a number of areas into which scenario generation can be expanded are reviewed. These focus on additional elements of both the training environment (e.g., buildings, interiors, etc.) and the training process (e.g., scenario write-ups, etc.).
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-3151 |
| Date | 01 January 2012 |
| Creators | Martin, Glenn Andrew |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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