497 citations · 878 across the 16 of their papers we have counts for
5 papers · 1 filter
Beyond Prompts: Exploring the Design Space of Mixed-Initiative Co-Creativity Systems
Zhiyu Lin, Upol Ehsan, Rohan Agarwal +3
Generative Artificial Intelligence systems have been developed for image, code, story, and game generation with the goal of facilitating human creativity. Recent work on neural gen…
Experiential Explanations for Reinforcement Learning
Amal Alabdulkarim, Madhuri Singh, Gennie Mansi +3
Reinforcement learning (RL) systems can be complex and non-interpretable, making it challenging for non-AI experts to understand or intervene in their decisions. This is due in par…
Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions
Upol Ehsan, Pradyumna Tambwekar, Larry Chan +2
Automated rationale generation is an approach for real-time explanation generation whereby a computational model learns to translate an autonomous agent's internal state and action…
Guiding Reinforcement Learning Exploration Using Natural Language
Brent Harrison, Upol Ehsan, Mark O. Riedl
In this work we present a technique to use natural language to help reinforcement learning generalize to unseen environments. This technique uses neural machine translation, specif…
Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations
Upol Ehsan, Brent Harrison, Larry Chan +1
We introduce AI rationalization, an approach for generating explanations of autonomous system behavior as if a human had performed the behavior. We describe a rationalization techn…