Predicting the behavior of interacting humans by fusing data from multiple sources

Erik J. Schlicht, Ritchie Lee, David H. Wolpert, Mykel J. Kochenderfer, Brendan Tracey

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

2 Scopus citations

Abstract

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 different conditions.

Original languageEnglish (US)
Title of host publicationUncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012
Pages756-764
Number of pages9
StatePublished - Dec 1 2012
Event28th Conference on Uncertainty in Artificial Intelligence, UAI 2012 - Catalina Island, CA, United States
Duration: Aug 15 2012Aug 17 2012

Publication series

NameUncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012

Other

Other28th Conference on Uncertainty in Artificial Intelligence, UAI 2012
CountryUnited States
CityCatalina Island, CA
Period8/15/128/17/12

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