Sampling Methods for Action Selection in Influence Diagrams

Proceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence, AAAI 2000

Volumn , Issue , 2000, Pages 378-385

  Ortiz, Luis E ^a   Kaelbling, Leslie Pack ^b  

^a BROWN UNIVERSITY   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

% REDUCTIONS; ACTION SELECTION; INFLUENCE DIAGRAM; NEAR-OPTIMAL; NUMBER OF SAMPLES; OPTIMAL ACTIONS; PROBABILISTIC GUARANTEES; SAMPLE SIZES; SAMPLING METHOD; SEQUENTIAL ANALYSIS;

HEURISTIC METHODS;

EID: 84943343220     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Bechhofer, R. E.; Santner, T. J.; and Goldsman, D. M. 1995. Design and analysis of experiments for statistical selection, screening and multiple comparisons. Wiley.
2. Bernstein, S. 1946. The Theory of Probabilities. Gastehizdat Publishing House, Moscow.
3. Bielza, C.; Müller, P.; and Insua, D. R. 1999. Monte Carlo methods for decision analysis with applications to influence diagrams. Management Science. Forthcoming.
4. Chang, J. Y., and Hsu, J. C. 1992. Optimal designs for multiple comparisons with the best. Journal of Statistical Planning and Inference 30:45-62.
5. Charnes, J. M., and Shenoy, P. P. 1999. A forward Monte Carlo method for solving Influence diagrams using local computation. School of Business, University of Kansas, Working Paper No. 273.
6. Devroye, L.; Györfi, L.; and Lugosi, G. 1996. A Probabilistic Theory of Pattern Recognition. Springer.
7. Fung, R., and Chang, K.-C. 1989. Weighting and integrating evidence for stochastic simulation in Bayesian networks. In Proceedings of the Fifth Workshop on Uncertainty in Artificial Intelligence, 112-117.
8. Geweke, J. 1989. Bayesian inference in econometric models using Monte Carlo integration. Econometrica 57(6):1317-1339.
9. Hoeffding, W. 1963. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association 58(301):13-30.
10. Hsu, J. C. 1981. Simultaneous confidence intervals for all distances from the”best”. Annals of Statistics 9(5):1026-1034.
11. Hsu, J. C. 1996. Multiple Comparisons: Theory and Methods. Chapman and Hall.
12. Jensen, F. V. 1996. An Introduction to Bayesian Networks. UCL Press.
13. Kearns, M.; Mansour, Y.; and Ng, A. Y. 1999. A sparse sampling algorithm for near-optimal planning in large Markov decision processes. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1324-1331. Menlo Park, Calif.: International Joint Conference on Artificial Intelligence, Inc.
14. Matejcik, F. J., and Nelson, B. L. 1995. Two-stage multiple comparisons with the best for computer simulation. Operations Research 43(4):633-640.
15. Murphy, K. P. 1999. Bayes net toolbox for Matlab. Available from http://www.cs.berkeley.edu/~murphyk/Bayes/bnt.html.
16. Ortiz, L. E., and Kaelbling, L. P. 2000. Adaptive importance sampling for estimation in structured domains. Under review.
17. Ortiz, L. E. 2000. Selecting approximately-optimal actions in complex structured domains. Technical Report CS-00-05, Computer Science Department, Brown University.
18. Shachter, R. D., and Peot, M. A. 1989. Simulation approaches to general probabilistic inference on belief networks. In Proceedings of the Fifth Workshop on Uncertainty in Artificial Intelligence, 311-318.