6 citations · 10 across the 3 of their papers we have counts for
4 papers
Beyond Value: CHECKLIST for Testing Inferences in Planning-Based RL
Kin-Ho Lam, Delyar Tabatabai, Jed Irvine +4
Reinforcement learning (RL) agents are commonly evaluated via their expected value over a distribution of test scenarios. Unfortunately, this evaluation approach provides limited e…
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…
Multi-Label Classifier Chains for Bird Sound
Forrest Briggs, Xiaoli Z. Fern, Jed Irvine
Bird sound data collected with unattended microphones for automatic surveys, or mobile devices for citizen science, typically contain multiple simultaneously vocalizing birds of di…