158 citations · 534 across the 33 of their papers we have counts for
6 papers · 1 filter
Inherently Explainable Reinforcement Learning in Natural Language
Xiangyu Peng, Mark O. Riedl, Prithviraj Ammanabrolu
We focus on the task of creating a reinforcement learning agent that is inherently explainable -- with the ability to produce immediate local explanations by thinking out loud whil…
Situated Dialogue Learning through Procedural Environment Generation
Prithviraj Ammanabrolu, Renee Jia, Mark O. Riedl
We teach goal-driven agents to interactively act and speak in situated environments by training on generated curriculums. Our agents operate in LIGHT (Urbanek et al. 2019) -- a lar…
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…
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…
Situated Language Learning via Interactive Narratives
Prithviraj Ammanabrolu, Mark O. Riedl
This paper provides a roadmap that explores the question of how to imbue learning agents with the ability to understand and generate contextually relevant natural language in servi…