Bayesian network models for generation of crisis management training scenarios

Eugene Grois, William H. Hsu, Mikhail Voloshin, David C. Wilkins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

We present a noisy-OR Bayesian network model for simulation-based training, and an efficient search-based algorithm for automatic synthesis of plausible training scenarios from constraint specifications. This randomized algorithm for approximate causal inference is shown to outperform other randomized methods, such as those based on perturbation of the maximally plausible scenario. It has the added advantage of being able to generate acceptable scenarios (based on a maximum penalized likelihood criterion) faster than human subject matter experts, and with greater diversity than deterministic inference. We describe a field-tested interactive training system for crisis management and show how our model can be applied offline to produce scenario specifications. We then evaluate the performance of our automatic scenario generator and compare its results to those achieved by human instructors, stochastic simulation, and maximum likelihood inference. Finally, we discuss the applicability of our system and framework to a broader range of modeling problems for computer-assisted instruction.

Original languageEnglish (US)
Title of host publicationInnovative Applications of Artificial Intelligence - Conference Proceedings
Editors Anon
PublisherAAAI
Pages1113-1120
Number of pages8
StatePublished - Jan 1 1998
EventProceedings of the 1998 10th Conference on Innovative Applications of Artificial Intelligence, IAAI - Madison, WI, USA
Duration: Jul 26 1998Jul 30 1998

Other

OtherProceedings of the 1998 10th Conference on Innovative Applications of Artificial Intelligence, IAAI
CityMadison, WI, USA
Period7/26/987/30/98

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