Everitt, Tom

17 publications

ICML 2025 General Agents Need World Models Jonathan Richens, Tom Everitt, David Abel

ICML 2025 The Limits of Predicting Agents from Behaviour Alexis Bellot, Jonathan Richens, Tom Everitt

AAAI 2024 Discovering Agents (Abstract Reprint) Zachary Kenton, Ramana Kumar, Sebastian Farquhar, Jonathan Richens, Matt MacDermott, Tom Everitt

NeurIPS 2024 Measuring Goal-Directedness Matt MacDermott, James Fox, Francesco Belardinelli, Tom Everitt

ICMLW 2024 Measuring Goal-Directedness Matt MacDermott, James Fox, Francesco Belardinelli, Tom Everitt

AAAI 2024 Reasoning About Causality in Games (Abstract Reprint) Lewis Hammond, James Fox, Tom Everitt, Ryan Carey, Alessandro Abate, Michael J. Wooldridge

ICLR 2024 Robust Agents Learn Causal World Models Jonathan Richens, Tom Everitt

NeurIPS 2023 Honesty Is the Best Policy: Defining and Mitigating AI Deception Francis Ward, Francesca Toni, Francesco Belardinelli, Tom Everitt

UAI 2023 Human Control: Definitions and Algorithms Ryan Carey, Tom Everitt

AAAI 2022 A Complete Criterion for Value of Information in Soluble Influence Diagrams Chris van Merwijk, Ryan Carey, Tom Everitt

AAAI 2022 Path-Specific Objectives for Safer Agent Incentives Sebastian Farquhar, Ryan Carey, Tom Everitt

AAAI 2022 Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness Carolyn Ashurst, Ryan Carey, Silvia Chiappa, Tom Everitt

AAAI 2021 Agent Incentives: A Causal Perspective Tom Everitt, Ryan Carey, Eric D. Langlois, Pedro A. Ortega, Shane Legg

AAAI 2021 How RL Agents Behave When Their Actions Are Modified Eric D. Langlois, Tom Everitt

IJCAI 2018 AGI Safety Literature Review Tom Everitt, Gary Lea, Marcus Hutter

IJCAI 2017 Count-Based Exploration in Feature Space for Reinforcement Learning Jarryd Martin, Suraj Narayanan Sasikumar, Tom Everitt, Marcus Hutter

IJCAI 2017 Reinforcement Learning with a Corrupted Reward Channel Tom Everitt, Victoria Krakovna, Laurent Orseau, Shane Legg