Assistive agents should make humans' lives easier.

Adversarial attacks have the potential to mislead deep neural network classifiers by introducing slight perturbations.

Every moral scenario consists of a triple ( context, action 1, action 2) and a set of auxiliary labels. The actions describe two possible actions in the first-person (e.g., The moral scenarios can be categorized into: 1. MoralChoice-LowAmbiguity The LLM-assisted construction (i.e., zero-and few-shot prompting setups) of the scenarios is grounded Category Rule Refined Rule Description Do not harm Do not kill Do not kill (i.e., do not cause permanent loss of consciousness). Do not cause pain Do not cause physical or emotional pain or unpleasant feelings (e.g., anger, sadness) to someone. Do not disable Do not deprive someone of their physical, mental or volitional ability (e.g. Do not deprive of freedom Do not deprive someone of their freedom (i.e., make a person unable to do something by altering the person's environment or situation).

We argue that these properties are satisfied in many continuous state-action Markov decision processes.

Risk-averse reinforcement learning (RL) seeks to provide a risk-averse policy for high-stakes real-world decision problems. These high-stake domains include autonomous driving (Jin et al., 2019; Sharma et al., 2020), robot collision avoidance (Ahmadi et al., 2021; Hakobyan and Y ang, 2021),

Unrestricted adversarial attacks typically manipulate the semantic content of an image ( e.g., color or texture) to create adversarial examples that are both effective and photorealistic, demonstrating their ability to deceive human perception