163 citations · 172 across the 4 of their papers we have counts for
5 papers
Identifying Reasoning Flaws in Planning-Based RL Using Tree Explanations
Kin-Ho Lam, Zhengxian Lin, Jed Irvine +5
Enabling humans to identify potential flaws in an agent's decision making is an important Explainable AI application. We consider identifying such flaws in a planning-based deep re…
Explaining Reinforcement Learning to Mere Mortals: An Empirical Study
Andrew Anderson, Jonathan Dodge, Amrita Sadarangani +6
We present a user study to investigate the impact of explanations on non-experts' understanding of reinforcement learning (RL) agents. We investigate both a common RL visualization…
Toward Foraging for Understanding of StarCraft Agents: An Empirical Study
Sean Penney, Jonathan Dodge, Claudia Hilderbrand +3
Assessing and understanding intelligent agents is a difficult task for users that lack an AI background. A relatively new area, called "Explainable AI," is emerging to help address…
How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games
Jonathan Dodge, Sean Penney, Claudia Hilderbrand +2
How should an AI-based explanation system explain an agent's complex behavior to ordinary end users who have no background in AI? Answering this question is an active research area…
Visualizing and Understanding Atari Agents
Sam Greydanus, Anurag Koul, Jonathan Dodge +1
While deep reinforcement learning (deep RL) agents are effective at maximizing rewards, it is often unclear what strategies they use to do so. In this paper, we take a step toward…