List of Publications

[EKO+17] Tom Everitt, Victoria Krakovna, Laurent Orseau, Marcus Hutter, and Shane Legg. Reinforcement learning with corrupted reward signal. In IJCAI International Joint Conference on Artificial Intelligence, 2017.
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[MSEH17] Jarryd Martin, Suraj Sasikumar, Tom Everitt, and Marcus Hutter. Count-Based Exploration in Feature Space for Reinforcement Learning. In IJCAI International Joint Conference on Artificial Intelligence, 2017.
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[EH17] Tom Everitt and Marcus Hutter. Universal artificial intelligence: Practical agents and fundamental challengs. In Foundations of Trusted Autonomy. Springer, 2017. To appear.
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[EFDH16] Tom Everitt, Daniel Filan, Mayank Daswani, and Marcus Hutter. Self-modification of policy and utility function in rational agents. In Bas Steunebrink, Pei Wang, and Ben Goertzel, editors, Artificial General Intelligence: 9th International Conference, AGI 2016, New York, NY, USA, July 16-19, 2016, Proceedings, pages 1--11, Cham, 2016. Springer International Publishing.
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[EH16] Tom Everitt and Marcus Hutter. Avoiding wireheading with value reinforcement learning. In Bas Steunebrink, Pei Wang, and Ben Goertzel, editors, Artificial General Intelligence: 9th International Conference, AGI 2016, New York, NY, USA, July 16-19, 2016, Proceedings, pages 12--22, Cham, 2016. Springer International Publishing. Source code.
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[MEH16] Jarryd Martin, Tom Everitt, and Marcus Hutter. Death and suicide in universal artificial intelligence. In Bas Steunebrink, Pei Wang, and Ben Goertzel, editors, Artificial General Intelligence: 9th International Conference, AGI 2016, New York, NY, USA, July 16-19, 2016, Proceedings, pages 23--32, Cham, 2016. Springer International Publishing.
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[ELH15] Tom Everitt, Jan Leike, and Marcus Hutter. Sequential extensions of causal and evidential decision theory. In Toby Walsh, editor, Algorithmic Decision Theory: 4th International Conference, ADT 2015, Lexington, KY, USA, September 27-30, 2015, Proceedings, pages 205--221, Cham, 2015. Springer International Publishing. Source code.
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[EH15a] Tom Everitt and Marcus Hutter. Analytical results on the BFS vs. DFS algorithm selection problem. Part I: Tree search. In Bernhard Pfahringer and Jochen Renz, editors, AI 2015: Advances in Artificial Intelligence: 28th Australasian Joint Conference, Canberra, ACT, Australia, November 30 -- December 4, 2015, Proceedings, pages 157--165, Cham, 2015. Springer International Publishing. Source code.
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[EH15b] Tom Everitt and Marcus Hutter. Analytical results on the BFS vs. DFS algorithm selection problem. Part II: Graph search. In Bernhard Pfahringer and Jochen Renz, editors, AI 2015: Advances in Artificial Intelligence: 28th Australasian Joint Conference, Canberra, ACT, Australia, November 30 -- December 4, 2015, Proceedings, pages 166--178, Cham, 2015. Springer International Publishing. Source code.
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[EH15c] Tom Everitt and Marcus Hutter. A topological approach to meta-heuristics: Analytical results on the BFS vs. DFS algorithm selection problem. Technical report, Australian National University, 2015.
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[ELH14] T. Everitt, T. Lattimore, and M. Hutter. Free lunch for optimisation under the universal distribution. In 2014 IEEE Congress on Evolutionary Computation (CEC), pages 167--174, July 2014.
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[AEH14] T. Alpcan, T. Everitt, and M. Hutter. Can we measure the difficulty of an optimization problem? In Information Theory Workshop (ITW), 2014 IEEE, pages 356--360, Nov 2014.
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[Eve13] Tom Everitt. Universal induction and optimisation: No free lunch? MSc thesis, Stockholm University, 2013.
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[Eve10] Tom Everitt. Automated Theorem Proving. BSc thesis, Stockholm University, 2010.
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