Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Principles of artificial intelligence
Principles of artificial intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Communications of the ACM
Automatic Speech Recognition: The Development of the Sphinx Recognition System
Automatic Speech Recognition: The Development of the Sphinx Recognition System
Efficient search-based inference for noisy-OR belief networks: topepsilon
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Automated instructor assistant for ship damage control
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A Multistrategy Approach to Classifier Learning from Time Series
Machine Learning - Special issue on multistrategy learning
Training for Crisis Decision-Making: Psychological Issues and Computer-Based Solutions
Journal of Management Information Systems
A scenario generation framework for automating instructional support in scenario-based training
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Prediction analysis of a wastewater treatment system using a Bayesian network
Environmental Modelling & Software
Bayesian-based scenario generation method for human activities
Proceedings of the 2013 ACM SIGSIM conference on Principles of advanced discrete simulation
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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.