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33 papers · 1 filter
Uncovering Systemic and Environment Errors in Autonomous Systems Using Differential Testing
Yashwanthi Anand, Rahil P Mehta, Manish Motwani +1
When an autonomous agent behaves undesirably, including failure to complete a task, it can be difficult to determine whether the behavior is due to a systemic agent error, such as…
Dynamic probabilistic logic models for effective abstractions in RL
Harsha Kokel, Arjun Manoharan, Sriraam Natarajan +2
State abstraction enables sample-efficient learning and better task transfer in complex reinforcement learning environments. Recently, we proposed RePReL (Kokel et al. 2021), a hie…
Algorithmic Ethics: Formalization and Verification of Autonomous Vehicle Obligations
Colin Shea-Blymyer, Houssam Abbas
We develop a formal framework for automatic reasoning about the obligations of autonomous cyber-physical systems, including their social and ethical obligations. Obligations, permi…
Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning
Matthew L. Olson, Roli Khanna, Lawrence Neal +2
Counterfactual explanations, which deal with "why not?" scenarios, can provide insightful explanations to an AI agent's behavior. In this work, we focus on generating counterfactua…
Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions
Zhengxian Lin, Kim-Ho Lam, Alan Fern
We investigate a deep reinforcement learning (RL) architecture that supports explaining why a learned agent prefers one action over another. The key idea is to learn action-values…
Avoiding Side Effects in Complex Environments
Alexander Matt Turner, Neale Ratzlaff, Prasad Tadepalli
Reward function specification can be difficult. Rewarding the agent for making a widget may be easy, but penalizing the multitude of possible negative side effects is hard. In toy…