Mark Riedl

Papers

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About

Mark Riedl is a pioneering researcher in artificial intelligence, focusing on the intersection of machine learning, human-robot interaction, and computational creativity. His work centers on enabling robots and AI systems to learn from natural human instruction, moving beyond rigid programming to intuitive, explanation-driven learning. A key contribution is his research on learning from explanations using sentiment and advice in reinforcement learning (2016, 83 citations), which demonstrates how non-expert humans can teach robots through natural language and feedback, making AI more accessible. Riedl is also a leading figure in computational narrative and story generation, with notable work such as "Ambient Adventures: Teaching ChatGPT on Developing Complex Stories" (2023), which explores how large language models can engage in imaginative play and create rich, evolving narratives. This research pushes the boundaries of AI creativity, envisioning robots that can participate in human-like storytelling and imaginary scenarios. With a career spanning over two decades, Riedl has shaped how AI systems understand, generate, and learn from stories, earning recognition for bridging technical AI advances with human-centered interaction. His work continues to inspire students and researchers in AI, robotics, and creative computing.