Publications (10)
Let Me At Least Learn What You Really Like: Dealing With Noisy Humans When Learning Preferences
Sriram Gopalakrishnan, Utkarsh Soni
Learning the preferences of a human improves the quality of the interaction with the human. The number of queries available to learn preferences maybe limited especially when inter…
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations
Sarath Sreedharan, Utkarsh Soni, Mudit Verma +2
As increasingly complex AI systems are introduced into our daily lives, it becomes important for such systems to be capable of explaining the rationale for their decisions and allo…
Not all users are the same: Providing personalized explanations for sequential decision making problems
Utkarsh Soni, Sarath Sreedharan, Subbarao Kambhampati
There is a growing interest in designing autonomous agents that can work alongside humans. Such agents will undoubtedly be expected to explain their behavior and decisions. While g…
Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion
Utkarsh Soni, Nupur Thakur, Sarath Sreedharan +4
There is a growing interest in developing automated agents that can work alongside humans. In addition to completing the assigned task, such an agent will undoubtedly be expected t…
PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Models
Aditi Mishra, Utkarsh Soni, Anjana Arunkumar +3
Large Language Models (LLMs) have gained widespread popularity due to their ability to perform ad-hoc Natural Language Processing (NLP) tasks with a simple natural language prompt.…
Same Stats, Different Graphs: Exploring the Space of Graphs in Terms of Graph Properties
Hang Chen, Vahan Huroyan, Utkarsh Soni +3
Data analysts commonly utilize statistics to summarize large datasets. While it is often sufficient to explore only the summary statistics of a dataset (e.g., min/mean/max), Anscom…