| Scalable agent alignment via reward modeling: a research direction J Leike, D Krueger, T Everitt, M Martic, V Maini, S Legg arXiv preprint arXiv:1811.07871, 2018 | 386 | 2018 |
| AI safety gridworlds J Leike, M Martic, V Krakovna, PA Ortega, T Everitt, A Lefrancq, L Orseau, ... arXiv preprint arXiv:1711.09883, 2017 | 365 | 2017 |
| Alignment of language agents Z Kenton, T Everitt, L Weidinger, I Gabriel, V Mikulik, G Irving arXiv preprint arXiv:2103.14659, 2021 | 173 | 2021 |
| AGI safety literature review T Everitt, G Lea, M Hutter International Joint Conference on AI (IJCAI), 2018 | 164 | 2018 |
| Count-based exploration in feature space for reinforcement learning J Martin, SN Sasikumar, T Everitt, M Hutter International Joint Conference on AI (IJCAI), 2017 | 152 | 2017 |
| Reinforcement Learning with Corrupted Reward Channel T Everitt, V Krakovna, L Orseau, M Hutter, S Legg 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017 | 132 | 2017 |
| Specification gaming: the flip side of AI ingenuity V Krakovna, J Uesato, V Mikulik, M Rahtz, T Everitt, R Kumar, Z Kenton, ... DeepMind Blog 3, 2020 | 124 | 2020 |
| Reward tampering problems and solutions in reinforcement learning: A causal influence diagram perspective T Everitt, M Hutter, R Kumar, V Krakovna Synthese, 2021 | 106 | 2021 |
| Shaking the foundations: delusions in sequence models for interaction and control PA Ortega, M Kunesch, G Delétang, T Genewein, J Grau-Moya, J Veness, ... arXiv preprint arXiv:2110.10819, 2021 | 65 | 2021 |
| Agent incentives: A causal perspective T Everitt, R Carey, ED Langlois, PA Ortega, S Legg Proceedings of the AAAI Conference on Artificial Intelligence 35 (13), 11487 …, 2021 | 60 | 2021 |
| Avoiding wireheading with value reinforcement learning T Everitt, M Hutter International Conference on Artificial General Intelligence (AGI), 12-22, 2016 | 53 | 2016 |
| Robust agents learn causal world models J Richens, T Everitt The Twelfth International Conference on Learning Representations, 2024 | 47 | 2024 |
| Towards safe artificial general intelligence T Everitt PQDT-Global, 2019 | 41 | 2019 |
| Discovering agents Z Kenton, R Kumar, S Farquhar, J Richens, M MacDermott, T Everitt Artificial Intelligence 322, 103963, 2023 | 39 | 2023 |
| Universal artificial intelligence: Practical agents and fundamental challenges T Everitt, M Hutter Foundations of trusted autonomy, 15-46, 2018 | 37 | 2018 |
| Self-modification of policy and utility function in rational agents T Everitt, D Filan, M Daswani, M Hutter International Conference on Artificial General Intelligence (AGI), 1-11, 2016 | 34 | 2016 |
| Understanding agent incentives using causal influence diagrams. Part I: Single action settings T Everitt, PA Ortega, E Barnes, S Legg arXiv preprint arXiv:1902.09980, 2019 | 33 | 2019 |
| Honesty is the best policy: defining and mitigating AI deception F Ward, F Toni, F Belardinelli, T Everitt Advances in neural information processing systems 36, 2313-2341, 2023 | 31 | 2023 |
| Path-specific objectives for safer agent incentives S Farquhar, R Carey, T Everitt Proceedings of the AAAI Conference on Artificial Intelligence 36 (9), 9529-9538, 2022 | 30 | 2022 |
| A game-theoretic analysis of the off-switch game T Wängberg, M Böörs, E Catt, T Everitt, M Hutter Artificial General Intelligence: 10th International Conference, AGI 2017 …, 2017 | 27 | 2017 |
Marcus HutterResearcher@DeepMind & Professor at ANUVerified email at anu.edu.au
