| AI safety gridworlds J Leike, M Martic, V Krakovna, PA Ortega, T Everitt, A Lefrancq, L Orseau, ... arXiv preprint arXiv:1711.09883, 2017 | 243 | 2017 |
| 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 | 115 | 2018 |
| AGI safety literature review T Everitt, G Lea, M Hutter International Joint Conference on AI (IJCAI), 2018 | 94 | 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 | 91 | 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 | 83 | 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, 2020 | 39 | 2020 |
| Reward tampering problems and solutions in reinforcement learning: A causal influence diagram perspective T Everitt, M Hutter, R Kumar, V Krakovna Synthese, 2021 | 36 | 2021 |
| Avoiding wireheading with value reinforcement learning T Everitt, M Hutter International Conference on Artificial General Intelligence (AGI), 12-22, 2016 | 32 | 2016 |
| Towards safe artificial general intelligence T Everitt PQDT-Global, 2019 | 26 | 2019 |
| 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 | 26 | 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 | 24 | 2019 |
| Universal artificial intelligence T Everitt, M Hutter Foundations of trusted autonomy, 15-46, 2018 | 23 | 2018 |
| Modeling AGI safety frameworks with causal influence diagrams T Everitt, R Kumar, V Krakovna, S Legg arXiv preprint arXiv:1906.08663, 2019 | 21 | 2019 |
| Alignment of language agents Z Kenton, T Everitt, L Weidinger, I Gabriel, V Mikulik, G Irving arXiv preprint arXiv:2103.14659, 2021 | 18 | 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 | 15 | 2021 |
| Artificial general intelligence T Everitt, B Goertzel, A Potapov Lecture Notes in Artificial Intelligence. Heidelberg: Springer, 2017 | 15 | 2017 |
| A game-theoretic analysis of the off-switch game T Wängberg, M Böörs, E Catt, T Everitt, M Hutter International Conference on Artificial General Intelligence, 167-177, 2017 | 14 | 2017 |
| Analytical results on the BFS vs. DFS algorithm selection problem. Part I: tree search T Everitt, M Hutter Australasian Joint Conference on Artificial Intelligence, 157-165, 2015 | 14 | 2015 |
| The incentives that shape behaviour R Carey, E Langlois, T Everitt, S Legg arXiv preprint arXiv:2001.07118, 2020 | 12 | 2020 |
| Death and suicide in universal artificial intelligence J Martin, T Everitt, M Hutter International Conference on Artificial General Intelligence, 23-32, 2016 | 12 | 2016 |
Marcus HutterResearcher@DeepMind & Professor at ANUVerified email at anu.edu.au
