A simple distribution-free approach to the max k-Armed bandit problem

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4204 LNCS, Issue , 2006, Pages 560-574

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

^a CENTER FOR THE NEURAL BASIS OF COGNITION   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CONSTRAINT THEORY; HEURISTIC METHODS; OPTIMIZATION; SCHEDULING; SET THEORY;

DISTRIBUTION-FREE APPROACHES; GENERALIZED EXTREME VALUE (GEV); K-ARMED BANDIT PROBLEMS; MAXIMAL TIME LAGS;

PROBLEM SOLVING;

EID: 33750375100     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11889205_40     Document Type: Conference Paper

References (13)

1. Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47:235-256, 2002a.
2. Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, and Robert E. Schapire. The non-stochastic multiarmed bandit problem. SIAM Journal on Computing, 32(1):48-77, 2002b.
3. Donald. A. Berry and Bert Fristedt. Bandit Problems: Sequential Allocation of Experiments. Chapman and Hall, London, 1986.
4. Vincent A. Cicirello and Stephen F. Smith. 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, pages 197-211, 2004.
5. Vincent A. Cicirello and Stephen F. Smith. The max k-armed bandit: A new model of exploration applied to search heuristic selection. In Proceedings of the Twentieth National Conference on Artificial Intelligence, pages 1355-1361, 2005.
6. Stuart Coles. An Introduction to Statistical Modeling of Extreme Values. Springer-Verlag, London, 2001.
7. Leslie P. Kaelbling. Learning in Embedded Systems. The MIT Press, Cambridge, MA, 1993.
8. Tze Leung Lai. Adaptive treatment allocation and the multi-armed bandit problem. The Annals of Statistics, 15(3):1091-1114, 1987.
9. Rolf H. Möhring, Andreas S. Schulz, Frederik Stork, and Marc Uetz. Solving project scheduling problems by minimum cut computations. Management Science, 49(3):330-350, 2003.
10. Klaus Neumann, Christoph Schwindt, and Jürgen Zimmerman. Project Scheduling with Time Windows and Scarce Resources. Springer-Verlag, 2002.
11. Herbert Robbins. Some aspects of sequential design of experiments. Bulletin of the American Mathematical Society, 58:527-535, 1952.
12. C. Schwindt. Generation of resource-constrained project scheduling problems with minimal and maximal time lags. Technical Report WIOR-489, Universität Karlsruhe, 1996.
13. Matthew J. Streeter and Stephen F. Smith. An asymptotically optimal algorithm for the max k-armed bandit problem. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, 2006.