Glenn A. Martin

The use of functional L-systems for scenario generation in serious games

So called serious games have used games (in a sense, virtual environments) for reasons other than entertainment. Particularly within the training community, they have garnered increasing attention over recent years. However, means of generating new scenarios that have increased training effectiveness has continued to be lacking. Because creating new scenarios is a time-intensive and costly exercise. existing scenarios are commonly reused with only minor changes, a practice that can hamper training effectiveness over time.n We have been pursuing a thrust of research in automated scenario generation. In this paper, we present our work in the use of Functional L-systems for generating scenarios. We first review some of our previous work in defining scenarios; then show how Functional L-systems are used to build up the scenarios.

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

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.n 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.n 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.

An after action review engine for training in multiple areas

The notion of after action review (AAR) is known in the military where it is used to develop a common picture of what happened and why. Recently, the concept has been rediscovered by other domains. Obviously, a review within these domains would be different. This paper addresses development of an AAR engine. By AAR engine we mean a system that provides the common functionalities across all AAR systems into a single foundation for training. Regardless of the domain, there are capabilities needed in an AAR system (e.g. recording and playback of scenario data). On the other hand, there are also features specific for each domain. In this paper we first review the infrastructure of our AAR engine. Then advantages of such a system for addressing various AAR systems are reviewed. Additional advanced functions are then presented and reviewed in light of how the engine can easily provide these enhancements.

Dynamic Terrain for Multiuser Real-Time Environments

Despite the pursuit for increased realism in VR training simulations and massively multiplayer online games, we find little deployed capability to modify terrain. Usually, terrain is stored in a static database and then preprocessed to support multiple levels of detail and to rectify features with the ground. Also, dynamic-terrain research has concentrated on graphics simulations for single users; weve found no research on systems that can support 500 or more user systems. Many obstacles to implementing large-scale dynamic-terrain simulations are hardware based such simulations need large amounts of memory and computing power. However, recent advances in computer hardware can help ameliorate these problems. In particular, high-performance computing clusters (HPCCs) offer an expandable set of computing elements, large amounts of memory and storage, and high-speed connections that remove many of the hardware limitations. Additionally, the industrys movement toward multi- or many-core machines indicates that research done today on high-performance computers (HPCs) could find its way into future commodity systems.

A generic after action review capability for game-based training

Recent years have seen a surge of interest in game-based training by the military. Game-based simulation possesses a number of potential benefits including decreased testbed complexity and cost, increased agility regarding both software and hardware, and the possibility of increased effectiveness relative to traditional training methods. One area of weakness in game-based training is the difficulty in supporting an after action review (AAR). The paper explores the emerging problems faced by systems attempting to facilitate AAR in game-based training scenarios. It presents an architecture that addresses or circumvents these issues in a flexible and game-agnostic manner, and details the limitations introduced by such an approach. A discussion on future work leveraging the plugin-based SOCRATES architecture to augment video for improved training is included.

Technological and usability-based aspects of distributed after action review in a game-based training setting

After action review (AAR) in the distributed setting provides for some unique problems. Some of these problems include remote facilitation of an after action review, keeping a lightweight infrastructure that can handle large amounts of throughput and allowing for different AAR sessions to be run simultaneously. This paper proposes a method for developing a facilitative infrastructure in the AAR setting while providing a solution that allows for syncing of multiple AAR software to one review session.

User interface and information management of scenarios

Scenario-based training has shown to be an effective instructional approach. However, it can be difficult for instructors to create scenarios as they are time-consuming to develop. This can result in the same scenarios being re-used, reducing training effectiveness. Furthermore, scenarios are based on training objectives and can vary in complexity. Researchers have investigated methods to simplify this process by assessing various approaches to scenario generation. Within such a tool, however, there needs to be a way to easily understand the different scenario components and their relationships within the scenario. This paper discusses the PYTHAGORAS (Procedural Yielding Techniques and Heuristics for Automated Generation of Objects with Related and Analogous Scenarios) system and specifically its editor used to create scenarios. We designed this system to simplify the process of scenario generation, providing a clearer understanding of the scenario components and relationships.

A testbed for exploring human-robot interaction with unmanned aerial and ground vehicles

Over the last twenty years, the emerging roles of unmanned aerial/ ground vehicles in the U.S. military presented a number of different research opportunities in usability and training, ranging from robotic control interfaces to human-robot team collaboration. In this paper we present a testbed that we developed as a flexible software platform to explore a variety of training and coordination issues with UXVs for military application.

Viability of mobile devices for training purposes

Mobile devices offer an advantageous platform on which to perform training simulations. However, they create new issues that are unique to mobile device development. First we will explore several reasons for using mobile devices for training simulators, instead of desktop or legacy-based systems. Once we have established the requirements of mobile simulators, we will discern some of the differences that arise between major mobile platforms in each development area. Finally we propose a solution that will address these differences and aid developers in creating cross-platform mobile simulations.