Chris Rosin

I live in San Diego and work at Parity Computing. I completed my Ph.D. in computer science at UCSD in 1997 and was a postdoc at The Scripps Research Institute. You can contact me at:
christopher.rosin@gmail.com.

Some current work:

• A new Morpion Solitaire record via Monte-Carlo search (updated May 2011)
• Greenpeep, a Computer Go Program

Publications:

• Rosin, C.D. (2011). Multi-armed bandits with episode context. Annals of Mathematics and Artificial Intelligence. DOI: 10.1007/s10472-011-9258-6
• Rosin, C.D. (2011). Nested Rollout Policy Adaptation for Monte Carlo Tree Search. Proceedings of IJCAI 2011 (Distinguished Paper)
• Rosin, C.D. (2010). "Multi-armed bandits with episode context." Proceedings of ISAIM 2010
• Rosin, C.D. (2000). "Sample complexity of model-based search." Journal of Computer and System Sciences 60:278-301.
• Rosin, C.D., Belew, R.K., Walker, W.L., Morris, G.M., Olson, A.J., Goodsell, D.S. (1999). Coevolution and subsite decomposition for the design of resistance-evading HIV-1 protease inhibitors. Journal of Molecular Biology 287(1):77-92.
• Rosin, C.D., Belew, R.K., Morris, G.M., Olson, A.J., and Goodsell, D.S. (1999). "Coevolutionary analysis of resistance-evading HIV-1 protease inhibitors." Proceedings of the National Academy of Sciences 96(4):1369-1374.
• Rosin, C.D. (1998). "Sample complexity of model-based search." Proceedings of the Eleventh Annual Conference on Computational Learning Theory. ACM.
• Rosin, C.D., Belew, R.K., Morris, G.M., Olson, A.J., and Goodsell, D.S. (1998). "Computational coevolution of antiviral drug resistance." Artificial Life 4:41-59.
• Rosin, C.D., Belew, R.K., Morris, G.M., Olson, A.J., and Goodsell, D.S. (1998). "Computational coevolution of antiviral drug resistance." Proceedings of the Sixth International Conference on Artificial Life. MIT Press.
• "Coevolutionary Search Among Adversaries" Christopher D. Rosin. Ph.D. Dissertation, University of California, San Diego, 1997.
• "A Comparison of Global and Local Search Methods in Drug Docking", Christopher D. Rosin, R. Scott Halliday, William E. Hart, and Richard K. Belew. Submitted version. Final version in the Proceedings of the Seventh International Conference on Genetic Algorithms. An older version of this paper is available as Technical Report #CS97-522.
• Technical Report #CS96-491: "New methods for Competitive Coevolution", Christopher D. Rosin and Richard K. Belew. Final version in Evolutionary Computation 5:1.
• ICGA 95 paper (submitted version): Two new methods are used to improve the performance of competitive co-evolution on several games, including Go. "Methods for Competitive Co-evolution: Finding Opponents Worth Beating", Christopher D. Rosin and Richard K. Belew. Final version in Proceedings of the Sixth International Conference on Genetic Algorithms. L.J. Eshelman, editor.
• COLT 96 paper (submitted version): A computational learning theoretic model of game learning is described, and sufficient conditions are given for polynomial-time learnability of perfect strategies. "A Competitive Approach to Game Learning", Christopher D. Rosin and Richard K. Belew. Final version in Proceedings of the Ninth Annual ACM Conference on Computational Learning Theory.
• An older unpublished extended abstract on Competitive Learning, a theoretical model of learning in a competitive environment.
• Niebur, E., Koch, C., and Rosin, C.D. (1993). "An oscillation-based model for the neuronal basis of attention." Vision Research 33:2789-2802.