Uncertainty in Artificial Intelligence
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Predicting the behavior of interacting humans by fusing data from multiple sources
Erik Schlicht, Ritchie Lee, David Wolpert, Mykel Kochenderfer, Brendan Tracey
Multi-fidelity methods combine inexpensive low-fidelity simulations with costly but highfidelity simulations to produce an accurate model of a system of interest at minimal cost. They have proven useful in modeling physical systems and have been applied to engineering problems such as wing-design optimization. During human-in-the-loop experimentation, it has become increasingly common to use online platforms, like Mechanical Turk, to run low-fidelity experiments to gather human performance data in an efficient manner. One concern with these experiments is that the results obtained from the online environment generalize poorly to the actual domain of interest. To address this limitation, we extend traditional multi-fidelity approaches to allow us to combine fewer data points from high-fidelity human-in-the-loop experiments with plentiful but less accurate data from low-fidelity experiments to produce accurate models of how humans interact. We present both model-based and model-free methods, and summarize the predictive performance of each method under dierent conditions.
Pages: 756-765
PS Link:
PDF Link: /papers/12/p756-schlicht.pdf
AUTHOR = "Erik Schlicht and Ritchie Lee and David Wolpert and Mykel Kochenderfer and Brendan Tracey",
TITLE = "Predicting the behavior of interacting humans by fusing data from multiple sources",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "756--765"

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