Free Lunch for optimisation under the universal distribution. - Tom Everitt; Tor Lattimore; Marcus Hutter (IEEE Congress on Evolutionary Computation, 2014)

Can we measure the difficulty of an optimization problem? - Tansu Alpcan; Tom Everitt; Marcus Hutter (ITW, 2014)

Sequential Extensions of Causal and Evidential Decision Theory. - Tom Everitt; Jan Leike; Marcus Hutter (ADT, 2015)

Analytical Results on the BFS vs. DFS Algorithm Selection Problem. Part I: Tree Search. - Tom Everitt; Marcus Hutter (Australasian Conference on Artificial Intelligence, 2015)

Analytical Results on the BFS vs. DFS Algorithm Selection Problem: Part II: Graph Search. - Tom Everitt; Marcus Hutter (Australasian Conference on Artificial Intelligence, 2015)

A Topological Approach to Meta-heuristics: Analytical Results on the BFS vs. DFS Algorithm Selection Problem. - Tom Everitt; Marcus Hutter (CoRR, 2015)

Self-Modification of Policy and Utility Function in Rational Agents. - Tom Everitt; Daniel Filan; Mayank Daswani; Marcus Hutter (AGI, 2016)

Avoiding Wireheading with Value Reinforcement Learning. - Tom Everitt; Marcus Hutter (AGI, 2016)

Death and Suicide in Universal Artificial Intelligence. - Jarryd Martin; Tom Everitt; Marcus Hutter (AGI, 2016)

A Game-Theoretic Analysis of the Off-Switch Game. - Tobias Wängberg; Mikael Böörs; Elliot Catt; Tom Everitt; Marcus Hutter (AGI, 2017)

Reinforcement Learning with a Corrupted Reward Channel. - Tom Everitt; Victoria Krakovna; Laurent Orseau; Shane Legg (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; Marcus Hutter; Shane Legg (CoRR, 2017)

AI Safety Gridworlds. - Jan Leike; Miljan Martic; Victoria Krakovna; Pedro A. Ortega; Tom Everitt; Andrew Lefrancq; Laurent Orseau; Shane Legg (CoRR, 2017)

AGI Safety Literature Review. - Tom Everitt; Gary Lea; Marcus Hutter (IJCAI, 2018)

Scalable agent alignment via reward modeling: a research direction. - Jan Leike; David Krueger 001; Tom Everitt; Miljan Martic; Vishal Maini; Shane Legg (CoRR, 2018)

Modeling AGI Safety Frameworks with Causal Influence Diagrams. - Tom Everitt; Ramana Kumar; Victoria Krakovna; Shane Legg (AISafety@IJCAI, 2019)

Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings. - Tom Everitt; Pedro A. Ortega; Elizabeth Barnes; Shane Legg (CoRR, 2019)

Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective. - Tom Everitt; Marcus Hutter (CoRR, 2019)

The Incentives that Shape Behaviour. - Ryan Carey; Eric D. Langlois 002; Tom Everitt; Shane Legg (CoRR, 2020)

REALab: An Embedded Perspective on Tampering. - Ramana Kumar; Jonathan Uesato; Richard Ngo; Tom Everitt; Victoria Krakovna; Shane Legg (CoRR, 2020)

Avoiding Tampering Incentives in Deep RL via Decoupled Approval. - Jonathan Uesato; Ramana Kumar; Victoria Krakovna; Tom Everitt; Richard Ngo; Shane Legg (CoRR, 2020)

Agent Incentives: A Causal Perspective. - Tom Everitt; Ryan Carey; Eric D. Langlois 002; Pedro A. Ortega; Shane Legg (AAAI, 2021)

How RL Agents Behave When Their Actions Are Modified. - Eric D. Langlois 002; Tom Everitt (AAAI, 2021)

Equilibrium Refinements for Multi-Agent Influence Diagrams: Theory and Practice. - Lewis Hammond; James Fox; Tom Everitt; Alessandro Abate; Michael J. Wooldridge (AAMAS, 2021)

PyCID: A Python Library for Causal Influence Diagrams. - James Fox; Tom Everitt; Ryan Carey; Eric D. Langlois 002; Alessandro Abate; Michael J. Wooldridge (SciPy, 2021)

Alignment of Language Agents. - Zachary Kenton; Tom Everitt; Laura Weidinger; Iason Gabriel; Vladimir Mikulik; Geoffrey Irving (CoRR, 2021)

Shaking the foundations: delusions in sequence models for interaction and control. - Pedro A. Ortega; Markus Kunesch; Grégoire Delétang; Tim Genewein; Jordi Grau-Moya; Joel Veness; Jonas Buchli; Jonas Degrave; Bilal Piot; Julien Pérolat; Tom Everitt; Corentin Tallec; et al. (CoRR, 2021)

Reward tampering problems and solutions in reinforcement learning: a causal influence diagram perspective. - Tom Everitt; Marcus Hutter; Ramana Kumar; Victoria Krakovna (Synth., 2021)

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

Path-Specific Objectives for Safer Agent Incentives. - Sebastian Farquhar; 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)

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

Human Control: Definitions and Algorithms. - Ryan Carey; Tom Everitt (UAI, 2023)

Characterising Decision Theories with Mechanised Causal Graphs. - Matt MacDermott; Tom Everitt; Francesco Belardinelli (CoRR, 2023)

Reasoning about causality in games. - Lewis Hammond; James Fox; Tom Everitt; Ryan Carey; Alessandro Abate; Michael J. Wooldridge (Artif. Intell., 2023)

Discovering agents. - Zachary Kenton; Ramana Kumar; Sebastian Farquhar; Jonathan Richens; Matt MacDermott; Tom Everitt (Artif. Intell., 2023)

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

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

The Reasons that Agents Act: Intention and Instrumental Goals. - Francis Rhys Ward; Matt MacDermott; Francesco Belardinelli; Francesca Toni; Tom Everitt (AAMAS, 2024)

Robust agents learn causal world models. - Jonathan Richens; Tom Everitt (ICLR, 2024)

Measuring Goal-Directedness. - Matt MacDermott; James Fox; Francesco Belardinelli; Tom Everitt (NeurIPS, 2024)

A Mechanism-Based Approach to Mitigating Harms from Persuasive Generative AI. - Seliem El-Sayed; Canfer Akbulut; Amanda McCroskery; Geoff Keeling; Zachary Kenton; Zaria Jalan; Nahema Marchal; Arianna Manzini; Toby Shevlane; Shannon Vallor; Daniel Susser; Matija Franklin; et al. (CoRR, 2024)

An Approach to Technical AGI Safety and Security. - Rohin Shah; Alex Irpan; Alexander Matt Turner; Anna Wang; Arthur Conmy; David Lindner; Jonah Brown-Cohen; Lewis Ho; Neel Nanda; Raluca Ada Popa; Rishub Jain; Rory Greig; et al. (CoRR, 2025)

Evaluating the Goal-Directedness of Large Language Models. - Tom Everitt; Cristina Garbacea; Alexis Bellot; Jonathan Richens; Henry Papadatos; Siméon Campos; Rohin Shah (CoRR, 2025)

General agents need world models. - Jonathan Richens; David Abel; Alexis Bellot; Tom Everitt (CoRR, 2025)

The Limits of Predicting Agents from Behaviour. - Alexis Bellot; Jonathan Richens; Tom Everitt (CoRR, 2025)