Marcus Hutter
Marcus Hutter [1] Marcus Hutter,
a German physicist and computer scientist, professor in the Research School of Computer Science at Australian National University. Before, he researched at IDSIA, Lugano, Switzerland in Jürgen Schmidhuber’s group. Marcus Hutter defended his PhD and BSc in physics from the Ludwig Maximilian University of Munich and a Habilitation, MSc, and BSc in computer science from Technical University of Munich. He is author of the AI-book Universal Artificial Intelligence [2] , a novel algorithmic information theory [3] perspective, also introducing the universal algorithmic agent called AIXI.
Selected Publications
2005 …
- Marcus Hutter (2005). Universal Artificial Intelligence. Sequential Decisions based on Algorithmic Probability, Springer
- Marcus Hutter (2007). Universal Algorithmic Intelligence: A mathematical top->down approach. Technical Report IDSIA-01-03 In Artificial General Intelligence, pdf
- Joel Veness, Kee Siong Ng, Marcus Hutter, David Silver (2009). A Monte Carlo AIXI Approximation, pdf
2010 …
- Joel Veness, Kee Siong Ng, Marcus Hutter, David Silver (2010). Reinforcement Learning via AIXI Approximation. AAAI-2010, pdf
- Tor Lattimore, Marcus Hutter, Vaibhav Gavane (2011). Universal Prediction of Selected Bits. Algorithmic Learning Theory, Lecture Notes in Computer Science 6925, Springer
- Tor Lattimore, Marcus Hutter (2011). Asymptotically Optimal Agents. Algorithmic Learning Theory, Lecture Notes in Computer Science 6925, Springer
- Tor Lattimore, Marcus Hutter (2011). Time Consistent Discounting. Algorithmic Learning Theory, Lecture Notes in Computer Science 6925, Springer
- Tor Lattimore, Marcus Hutter (2011). No Free Lunch versus Occam’s Razor in Supervised Learning. Solomonoff Memorial, Lecture Notes in Computer Science, Springer, arXiv:1111.3846 [7] [8]
- Joel Veness, Kee Siong Ng, Marcus Hutter, William Uther , David Silver (2011). A Monte-Carlo AIXI Approximation. JAIR, Vol. 40, pdf
- Tor Lattimore, Marcus Hutter (2012). PAC Bounds for Discounted MDPs. Algorithmic Learning Theory, arXiv:1202.3890 [9]
- Peter Auer, Marcus Hutter, Laurent Orseau (2013). Reinforcement Learning. Dagstuhl Reports, Vol. 3, No. 8, DOI: 10.4230/DagRep.3.8.1, URN: urn:nbn🇩🇪0030-drops-43409
- Tor Lattimore, Marcus Hutter (2014). Bayesian Reinforcement Learning with Exploration. Algorithmic Learning Theory, Lecture Notes in Computer Science 8776, Springer
2015 …
- Tom Everitt, Marcus Hutter (2015). Analytical Results on the BFS vs. DFS Algorithm Selection Problem. Part I: Tree Search. Australasian Conference on Artificial Intelligence, pdf
- Tom Everitt, Marcus Hutter (2015). Analytical Results on the BFS vs. DFS Algorithm Selection Problem: Part II: Graph Search. Australasian Conference on Artificial Intelligence
- Tom Everitt, Tor Lattimore, Marcus Hutter (2016). Free Lunch for Optimisation under the Universal Distribution. arXiv:1608.04544
- Marcus Hutter (2017). Universal Learning Theory. Encyclopedia of Machine Learning and Data Mining 2017, Springer
External Links
References
- ↑ HomePage of Marcus Hutter
- ↑ Marcus Hutter (2005). Universal Artificial Intelligence. Sequential Decisions based on Algorithmic Probability, Springer
- ↑ Algorithmic information theory - Scholarpedia
- ↑ The AIXI Model in One Line
- ↑ Publications of Marcus Hutter
- ↑ dblp: Marcus Hutter:
- ↑ No free lunch in search and optimization - Wikipedia
- ↑ Occam’s razor from Wikipedia
- ↑ Markov decision process from Wikipedia