Good Things Come in Trees: Emotion and Context Aware Behaviour Trees for Ethical Robotic Decision-Making

TL;DR

This paper presents an emotion and context-aware behavior tree framework for ethical decision-making in home robots, emphasizing safety, privacy, and transparency in robot responses to human requests.

Contribution

It introduces a novel emotion-based approach using behavior trees to improve ethical decision-making in robots, incorporating somatic markers and emotion inference.

Findings

Emotion-based heuristics improve safety in robot decisions

Behavior trees enable real-time reasoning and transparency

Context and privacy policies enhance ethical control

Abstract

Emotions guide our decision making process and yet have been little explored in practical ethical decision making scenarios. In this challenge, we explore emotions and how they can influence ethical decision making in a home robot context: which fetch requests should a robot execute, and why or why not? We discuss, in particular, two aspects of emotion: (1) somatic markers: objects to be retrieved are tagged as negative (dangerous, e.g. knives or mind-altering, e.g. medicine with overdose potential), providing a quick heuristic for where to focus attention to avoid the classic Frame Problem of artificial intelligence, (2) emotion inference: users' valence and arousal levels are taken into account in defining how and when a robot should respond to a human's requests, e.g. to carefully consider giving dangerous items to users experiencing intense emotions. Our emotion-based approach builds a foundation for the primary consideration of Safety, and is complemented by policies that support overriding based on Context (e.g. age of user, allergies) and Privacy (e.g. administrator settings). Transparency is another key aspect of our solution. Our solution is defined using behaviour trees, towards an implementable design that can provide reasoning information in real-time.