HCMD-zero: Learning Value Aligned Mechanisms from Data

no code implementations • 21 Feb 2022 • Jan Balaguer, Raphael Koster, Ari Weinstein, Lucy Campbell-Gillingham, Christopher Summerfield, Matthew Botvinick, Andrea Tacchetti

Our analysis shows HCMD-zero consistently makes the mechanism policy more and more likely to be preferred by human participants over the course of training, and that it results in a mechanism with an interpretable and intuitive policy.

Human-centered mechanism design with Democratic AI

no code implementations • 27 Jan 2022 • Raphael Koster, Jan Balaguer, Andrea Tacchetti, Ari Weinstein, Tina Zhu, Oliver Hauser, Duncan Williams, Lucy Campbell-Gillingham, Phoebe Thacker, Matthew Botvinick, Christopher Summerfield

Building artificial intelligence (AI) that aligns with human values is an unsolved problem.

reinforcement-learning Reinforcement Learning (RL)

Probing Physics Knowledge Using Tools from Developmental Psychology

no code implementations • 3 Apr 2018 • Luis Piloto, Ari Weinstein, Dhruva TB, Arun Ahuja, Mehdi Mirza, Greg Wayne, David Amos, Chia-Chun Hung, Matt Botvinick

While some work on this problem has taken the approach of building in components such as ready-made physics engines, other research aims to extract general physical concepts directly from sensory data.

Structure Learning in Motor Control:A Deep Reinforcement Learning Model

no code implementations • 21 Jun 2017 • Ari Weinstein, Matthew M. Botvinick

We present a new model of motor structure learning, approaching it from the point of view of deep reinforcement learning.

Model-based Reinforcement Learning reinforcement-learning +1