Richens, Jonathan

Improving the accuracy of medical diagnosis with causal machine learning

Richens, J. G., Lee, C. M., & Johri, S. (2020) · Nature Communications

Artificial Intelligence in HealthcareMachine Learning in Healthcare

Cited by 598

General agents contain world models

Richens, J., Abel, D., Bellot, A., & Everitt, T. (2025) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 30

Discovering agents

Kenton, Z., Kumar, R., Farquhar, S., Richens, J., MacDermott, M., & Everitt, T. (2023) · Artificial Intelligence

Bayesian Modeling and Causal InferenceLogic, Reasoning, and Knowledge

Cited by 17

Discovering Agents

Kenton, Z., Kumar, R., Farquhar, S., Richens, J., MacDermott, M., & Everitt, T. (2022) · arXiv

Bayesian Modeling and Causal InferenceLogic, Reasoning, and Knowledge

Cited by 7

Robust agents learn causal world models

Richens, J., & Everitt, T. (2024) · arXiv

Bayesian Modeling and Causal InferenceReinforcement Learning in Robotics

Cited by 5

Evaluating the Goal-Directedness of Large Language Models

Everitt, T., Garbacea, C., Bellot, A., Richens, J., Papadatos, H., Campos, S., & Shah, R. (2025) · arXiv

Topic ModelingAI-based Problem Solving and Planning

Cited by 1

Agency Is Frame-Dependent

Abel, D., Barreto, A., Bowling, M., Dabney, W., Dong, S., Hansen, S., Harutyunyan, A., Khetarpal, K., Lyle, C., Pascanu, R., et al. (2025) · arXiv

Philosophy of Biology and Scientific ExplanationEmbodied and Extended Cognition

Cited by 0