An architecture of a knowledge-based simulation engine

Erraguntla, M.; Benjamin, Perakath; Mayer, Richard J.

doi:10.1109/wsc.1994.717408

Cited by 8 publications

(4 citation statements)

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“…Knowledge Based Systems Inc. developed the Framework for Enabling Adaptive Scenario Generation for Training (FEAST) (Erraguntla et al, 1994). FEAST uses fine-grained analyses of the training context and knowledge modeling methods to automate the generation of dynamically adaptive training scenarios for the U.S. Air Force.…”

Section: Previous Workmentioning

confidence: 99%

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

Martin

Hughes

2010

Proceedings of the 2010 Spring Simulation Multiconference

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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.

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Section: Previous Workmentioning

confidence: 99%

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

Martin

Hughes

2010

Proceedings of the 2010 Spring Simulation Multiconference

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“…Knowledge Based Systems Inc. has been developing knowledge-based simulation architectures for more than a decade (e.g., Erraguntla et al, 1994). Their Framework for Enabling Adaptive Scenario Generation for Training (FEAST) uses very fine-grained analyses of the training context and knowledge modeling methods to automate the generation of dynamically adaptive training scenarios for the U.S. Air Force.…”

Section: Scenario Generation: Past Workmentioning

confidence: 99%

Automatic Scenario Generation through Procedural Modeling for Scenario-Based Training

et al. 2009

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We discuss our current efforts at developing automatic scenario generation software. We begin by explaining the rationale, and then review successful previous efforts. We discuss the lessons-learned from the past work, and the conceptual pieces that are required to generate operationally-valid scenarios that support effective training. We then present the conceptual design of our scenario generation approach, which uses novel procedural modeling approaches to ensure operational and training requirements are adequately met.Scenario-based training (SBT) is a highly-effective strategy for preparing individuals and teams for complex task domains. For maximum efficacy, SBT systems must include robust libraries of varied scenarios; however, the creation of validated, effective scenarios is time-consuming and must be carried out by highly-trained domain specialists, qualified instructors, and technology experts. The process is challenging and time-consuming, and therefore expensive. Thus, in practice, scenario libraries often fail to cover the optimal range of possible mission contexts. One way to facilitate the creation of more robust scenario libraries may be to automate the process of scenario generation. Automated scenario generation must be supported by appropriate algorithmic modeling approaches, as well as research-based heuristics to ensure that scenario outputs are domain-valid and pedagogically effective. In this paper, we explore both of these issues. From the perspective of computer science, we review procedural modeling techniques that can support automated scenario generation. From the perspective of training science, we discuss guidelines and research approaches for developing objective guidelines to ensure scenario effectiveness.

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“…An increasing volume of research has combined Artificial Intelligence (AI) and simulation in the past decade (Cho 2002 According to Erraguntla et al (1994), the goals of the KBS Engines are:…”

Section: Neural Network and Simulationmentioning

confidence: 99%

Using neural networks for goal driven simulation

Clavijo¹

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(n>3) and it does not have domain restrictions. The effectiveness of the framework was tested by observing the computational time required for obtaining responses and for training, and by assessing its accuracy for different scenarios. This framework has achieved the automation objective set by GDS under a shorter time frame, as it reduces the time from more than 42 hours to less than 14. A trained NN generates responses to queries almost instantaneously. However, it requires time re-building and re-training new NNs when changes are made to the system represented by the model. If these changes are rare, the payoff is worthy as this approach gives users more flexibility.

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An architecture of a knowledge-based simulation engine

Cited by 8 publications

References 0 publications

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

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

Automatic Scenario Generation through Procedural Modeling for Scenario-Based Training

Using neural networks for goal driven simulation

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