16 research outputs found
Neural Topic Modeling of Psychotherapy Sessions
In this work, we compare different neural topic modeling methods in learning
the topical propensities of different psychiatric conditions from the
psychotherapy session transcripts parsed from speech recordings. We also
incorporate temporal modeling to put this additional interpretability to action
by parsing out topic similarities as a time series in a turn-level resolution.
We believe this topic modeling framework can offer interpretable insights for
the therapist to optimally decide his or her strategy and improve psychotherapy
effectiveness.Comment: This work extends our research series in computational linguistics
for psychiatry (e.g. working alliance analysis in arXiv:2204.05522) with a
systematic investigation of neural topic modeling approaches to provide
interpretable insights in psychotherap
Language-Grounded Control for Coordinated Robot Motion and Speech
Recent advancements have enabled human-robot collaboration through physical
assistance and verbal guidance. However, limitations persist in coordinating
robots' physical motions and speech in response to real-time changes in human
behavior during collaborative contact tasks. We first derive principles from
analyzing physical therapists' movements and speech during patient exercises.
These principles are translated into control objectives to: 1) guide users
through trajectories, 2) control motion and speech pace to align completion
times with varying user cooperation, and 3) dynamically paraphrase speech along
the trajectory. We then propose a Language Controller that synchronizes motion
and speech, modulating both based on user cooperation. Experiments with 12
users show the Language Controller successfully aligns motion and speech
compared to baselines. This provides a framework for fluent human-robot
collaboration.Comment: Under review in ICRA 202
Migratable AI: Investigating Users’ Affect on Identity and Information Migration of a Conversational AI Agent
Twitter Can Predict Your Next Place of Visit
The present work focuses on predicting users' next place of visit using their past tweets. We hypothesize that tweets of the person have predictive power on his location and therefore can be used to predict his next place of visit. This problem is important for location based advertising and recommender based services. To predict the next place of visit, we calculate the probabilities of visiting different types of places using bank of binary classifiers and Markov models. More specifically, we train bank of binary classifiers on past tweets and calculated the probabilities of visiting next places. Since bank of binary classifiers is based on a bag-of-words model, to account for time of last visited place and place itself, we built Markov models for different time duration to calculate probabilities of visiting next place. Empirical evaluation shows that by combining the probabilities obtained from bank of binary classifiers and Markov models the accuracy of predicting next place increased from 65% to 80%
Social Interactions as Recursive MDPs
While machines and robots must interact with humans, providing them with social skills has been a largely overlooked topic. This is mostly a consequence of the fact that tasks such as navigation, command following, and even game playing are well-defined, while social reasoning still mostly re- mains a pre-theoretic problem. We demonstrate how social interactions can be effectively incorporated into MDPs (Markov decision processes) by reasoning recursively about the goals of other agents. In essence, our method extends the reward function to include a combination of physical goals (something agents want to accomplish in the configuration space, a traditional MDP) and social goals (something agents want to accomplish relative to the goals of other agents). Our Social MDPs allow specifying reward functions in terms of the estimated reward functions of other agents, modeling interactions such as helping or hindering another agent (by maximizing or minimizing the other agent’s reward) while bal- ancing this with the actual physical goals of each agent. Our formulation allows for an arbitrary function of another agent’s estimated reward structure and physical goals, enabling more complex behaviors such as politely hindering another agent or aggressively helping them. Extending Social MDPs in the same manner as I-POMDPs (Interactive-partially observed Markov decision processes) extension would enable interactions such as convincing another agent that something is true. To what extent the Social MDPs presented here and their potential Social POMDPs variant account for all possible social interactions is unknown, but having a precise mathematical model to guide questions about social in- teractions has both practical value (we demonstrate how to make zero-shot social inferences and one could imagine chatbots and robots guided by Social MDPs) and theoretical value by bringing the tools of MDP that have so successfully organized research around navigation to shed light on what social interactions really are given their extreme importance to human well-being and human civilization.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF – 1231216
Use of Molecular Simulation in Calculating a Characteristic Relative Growth Effect Curvature to Correlate Factors Influencing Crystalline Growth and Other Properties
The
following report outlines a simplified method to predict the
effect on the relative growth rates of crystal facets resulting from
different solvents. The method uses molecular dynamics (MD) techniques
which are coupled to a Monte Carlo (MC) scheme to generate distributions
of estimates of molecular binding energies at the crystal surface.
We then use these calculated binding energies to make inferences on
how solvent may affect the relative growth rate of the crystal facets
(i.e., solvent effect on growth). We support the analysis by revisiting
the growth of adipic acid. It is demonstrated that there is a remarkable
increase in the sensitivity of the expected values used to represent
the “solvent effect on growth” when a very simple correction
for the molecular size between solute and solvent is implemented into
the Monte Carlo scheme. The use of single point energy calculations
(potential energy) displays limited sensitivity to the expected solvent
effect in comparison to the use of distributions of MD derived values
(binding free energy). Thus, the combination of relative binding free
energy data and the proposed MC scheme is believed to be an effective
path forward to providing insight into a solvent or additive effect
on growth for more complex molecular systems that is simple to implement
and does not come at a significantly high computational expense. In
order to make an assessment of the data from simulation, plotting
of the relative growth effect curvature is also introduced
Migratable AI: Effect of identity and information migration on users' perception of conversational AI agents
© 2020 IEEE. Conversational AI agents are proliferating, embodying a range of devices such as smart speakers, smart displays, robots, cars, and more. We can envision a future where a personal conversational agent could migrate across different form factors and environments to always accompany and assist its user to support a far more continuous, personalized and collaborative experience. This opens the question of what properties of a conversational AI agent migrates across forms, and how it would impact user perception. To explore this, we developed a Migratable AI system where a user's information and/or the agent's identity can be preserved as it migrates across form factors to help its user with a task. We validated the system by designing a 2x2 between-subjects study to explore the effects of information migration and identity migration on user perceptions of trust, competence, likeability and social presence. Our results suggest that identity migration had a positive effect on trust, competence and social presence, while information migration had a positive effect on trust, competence and likeability. Overall, users report highest trust, competence, likeability and social presence towards the conversational agent when both identity and information were migrated across embodiments