Utkarsh Soni

I am a Computer Science Ph.D. student at the School of Computing and Augmented Intelligence, Arizona State University. I work in the Yochan research group directed by Prof Subbarao Kambhampati. My current research focuses on enabling preference specification, preference incorporation, and explanation generation in human-in-the-loop reinforcement learning systems. I have also worked on generating personalized explanations for robot's actions in multi-user settings; and using data visualization as a modality for effective human-AI communication.

Prior to my PhD, I completed my M.S. in Computer Science at ASU, where I worked in the Vader Lab under Prof Ross Maciejewski on graphical layout perception. I also worked on an urban employment tool meant for government decision makers to help them analyze multiple aspects of employment data in their region.

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We propose PRESCA (PREference Specification through Concept Acquisition), a system that allows users to specify their preferences in terms of concepts that they understand to a RL agent.

A method for generating contrastive explanations, in terms of user-specified concepts, for an agent in a sequential decision making setting.

End-to-end adaptive explanation generation system that learns different types of users, interacts with them and adjusts it's explanation on the fly.

Providing a visual analytics interface to question and thus gain trust in an autonomous agent's decision.

An adaptive agent that plans online to adapt to novelty introduced during stochastic multi-agent games

An active learning based method to learn user's preferences in sequential decision making tasks.

Modeling human's perception of graph properties in different graph layouts via a large scale user study

We show how summary statistic of graphs can be misleading and can be the same across widely varying graphs by a visual analytic tool