Machine Learning and Optimization

We report an approach to creating on-line acoustic synchrony by using a dynamic Bayesian network learned from prior recordings of child-child play to select from a predfinened space of robot speech in response to real-time measurement of the child's prosodic features.

Motivated by the original “ghosting” work, we showcase an automatic “data-driven ghosting” method using advanced machine learning methodologies applied to a season’s worth of tracking data from a recent professional league in soccer.

We developed four different protocols to investigate human spatial behavior or trust in robots.

We present PIP, an agent that crowdsources its own multimodal language behavior using a method we call semi-situated learning.

We introduce a static-dynamic scale to categorize level analysis strategies, which captures the extent that the analysis depends on player simulation.

In this paper, we propose a novel story generator, PLOTSHOT, capable of reasoning over discourse materials during fabula generation such that these materials meaningfully constrain the development of a causally and intentionally coherent story.

This technology can enable others to create novel multi-party interactions for entertainment where a limited number of keywords has to be recognized.

This paper examines the extent to which computer speech recognition errors for children’s speech can be attributed to common phonological effects associated with language acquisition.

Our results indicate that the considered acoustic features are related to engagement levels for both the child-child and child-robot interaction.