536 citations · 2k across the 52 of their papers we have counts for
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Explainability Pitfalls: Beyond Dark Patterns in Explainable AI
Upol Ehsan, Mark O. Riedl
To make Explainable AI (XAI) systems trustworthy, understanding harmful effects is just as important as producing well-designed explanations. In this paper, we address an important…
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations
Upol Ehsan, Samir Passi, Q. Vera Liao +4
Explainability of AI systems is critical for users to take informed actions. Understanding "who" opens the black-box of AI is just as important as opening it. We conduct a mixed-me…
Modeling Worlds in Text
Prithviraj Ammanabrolu, Mark O. Riedl
We provide a dataset that enables the creation of learning agents that can build knowledge graph-based world models of interactive narratives. Interactive narratives -- or text-adv…
Detecting and Adapting to Novelty in Games
Xiangyu Peng, Jonathan C. Balloch, Mark O. Riedl
Open-world novelty occurs when the rules of an environment can change abruptly, such as when a game player encounters "house rules". To address open-world novelty, game playing age…
Learning Knowledge Graph-based World Models of Textual Environments
Prithviraj Ammanabrolu, Mark O. Riedl
World models improve a learning agent's ability to efficiently operate in interactive and situated environments. This work focuses on the task of building world models of text-base…
Telling Stories through Multi-User Dialogue by Modeling Character Relations
Wai Man Si, Prithviraj Ammanabrolu, Mark O. Riedl
This paper explores character-driven story continuation, in which the story emerges through characters' first- and second-person narration as well as dialogue -- requiring models t…