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Across a wide variety of domains, artificial agents that can adapt and personalize to users have potential to improve and transform how social services are provided. Because of the need for personalized interaction data to drive this process, long-term (or longitudinal) interactions between users and agents, which unfold over a series of distinct interaction sessions, have attracted substantial research interest. In recognition of the expanded scope and structure of a long-term interaction, researchers are also adjusting the personalization models and algorithms used, orienting toward “continual learning” methods, which do not assume a stationary modeling target and explicitly account for the temporal context of training data. In parallel, researchers have also studied the effect of “multitask personalization,” an approach in which an agent interacts with users over multiple different tasks contexts throughout the course of a long-term interaction and learns personalized models of a user that are transferrable across these tasks. In this paper, we unite these two paradigms under the framework of “Lifelong Personalization,” analyzing the effect of multitask personalization applied to dynamic, non-stationary targets. We extend the multi-task personalization approach to the more complex and realistic scenario of modeling dynamic learners over time, focusing in particular on interactive scenarios in which the modeling agent plays an active role in teaching the student whose knowledge the agent is simultaneously attempting to model. Inspired by the way in which agents use active learning to select new training data based on domain context, we augment a Gaussian Process-based multitask personalization model with a mechanism to actively and continually manage its own training data, allowing a modeling agent to remove or reduce the weight of observed data from its training set, based on interactive context cues. We evaluate this method in a series of simulation experiments comparing different approaches to continual and multitask learning on simulated student data. We expect this method to substantially improve learning in Gaussian Process models in dynamic domains, establishing Gaussian Processes as another flexible modeling tool for Long-term Human-Robot Interaction (HRI) Studies. 

This pilot study investigated the feasibility of implementing child-friendly robots for administering clinical and educational assessments with young children. JIBO, a social robot, was used as a new interface to administer a letter and number naming task and the 3rd Goldman Fristoe Test of Articulation (GFTA-3). The reason for using these assessment materials is to develop robust automatic speech recognition (ASR) and automated social interaction systems that can aid in administering such assessments more efficiently. The voice of JIBO simulates interaction with a peer, and images and playful transitions are displayed on JIBO’s face/screen. Several preliminary observations with 15 pre-kindergarten and 18 kindergarten students included the rate of task completion and strategies to increase student participation. Changes to the length and prompt delivery of the assessment protocol were considered based on these observations, and further observations are planned for future work with an additional cohort of 43 prekindergarten and 50 kindergarten students. Recommendations are given to inform future implementations and analyses.