Learning Norms from Stories: A Prior for Value Aligned Agents

Abstract

Value alignment is a property of an intelligent agent indicating that it can only pursue goals and activities that are beneficial to humans. Traditional approaches to value alignment use imitation learning or preference learning to infer the values of humans by observing their behavior. We introduce a complementary technique in which a value aligned prior is learned from naturally occurring stories which encode societal norms. Training data is sourced from the childrens educational comic strip, Goofus and Gallant. In this work, we train multiple machine learning models to classify natural language descriptions of situations found in the comic strip as normative or non normative by identifying if they align with the main characters behavior. We also report the models performance when transferring to two unrelated tasks with little to no additional training on the new task.

arXiv commentAIES 2020

References in corpus (10)

• XLNet: Generalized Autoregressive Pretraining for Language Understanding
• Deep reinforcement learning from human preferences
• Fine-Tuning Language Models from Human Preferences
• Cooperative Inverse Reinforcement Learning
• Generative Adversarial Imitation Learning
• Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding
• Third-Person Imitation Learning
• Story Realization: Expanding Plot Events into Sentences
• APRIL: Active Preference-learning based Reinforcement Learning
• dAIrector: Automatic Story Beat Generation through Knowledge Synthesis

Cited by in corpus (2)

• HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks
• Author as Character and Narrator: Deconstructing Personal Narratives from the r/AmITheAsshole Reddit Community