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 language | English (US) |
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Title of host publication | Innovative Applications of Artificial Intelligence - Conference Proceedings |
Editors | Anon |
Publisher | AAAI |
Pages | 1113-1120 |
Number of pages | 8 |
State | Published - Jan 1 1998 |
Event | Proceedings of the 1998 10th Conference on Innovative Applications of Artificial Intelligence, IAAI - Madison, WI, USA Duration: Jul 26 1998 → Jul 30 1998 |
Other
Other | Proceedings of the 1998 10th Conference on Innovative Applications of Artificial Intelligence, IAAI |
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City | Madison, WI, USA |
Period | 7/26/98 → 7/30/98 |