Learning the Value Systems of Societies with Preference-based Multi-objective Reinforcement Learning

Value-aware AI should recognise human values and adapt to the value systems (value-based preferences) of different users. This requires acquiring computable representations of values, a process that can be prone to misspecification. The social nature of values demands their representation to adhere to multiple users while value systems are diverse, yet exhibit patterns among groups. In sequential decision making, efforts have been made towards personalization for different goals or values from demonstrations of diverse agents. However, these approaches demand manually designed features or lack value-based interpretability and/or adaptability to diverse user preferences.