An asymptotically optimal algorithm for the max k-armed bandit problem

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2006, Pages 135-142

  Streeter, Matthew J ^a   Smith, Stephen F ^b  

^a Carnegie Mellon University   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DISTRIBUTIONAL ASSUMPTIONS; K-ARMED BANDIT PROBLEM; OPTIMAL ALGORITHM;

ARTIFICIAL INTELLIGENCE; ASYMPTOTIC STABILITY; COMPUTATION THEORY; LEARNING SYSTEMS; PROBLEM SOLVING;

ALGORITHMS;

EID: 33750702842     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. Berry, D. A., and Fristedt, B. 1986. Bandit Problems: Sequential Allocation of Experiments. London: Chapman and Hall.
2. Cicirello, V. A., and Smith, S. F. 2004. Heuristic selection for stochastic search optimization: Modeling solution quality by extreme value theory. In Proceedings of the 10th International Conference on Principles and Practice of Constraint Programming, 197-211.
3. Cicirello, V. A., and Smith, S. F. 2005. The max k-armed bandit: A new model of exploration applied to search heuristic selection. In Proceedings of AAAI 2005, 1355-1361.
4. Coles, S. 2001. An Introduction to Statistical Modeling of Extreme Values. London: Springer-Verlag.
5. Fong, P. W. L. 1995. A quantitative study of hypothesis selection. In International Conference on Machine Learning, 226-234.
6. Kaelbling, L. P. 1993. Learning in Embedded Systems. Cambridge, MA: The MIT Press.
7. Robbins, H. 1952. Some aspects of sequential design of experiments. Bulletin of the American Mathematical Society 58:527-535.