Learning an optimal decision strategy in an influence diagram with latent variables

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Learning an optimal decision strategy in an influence diagram with latent variables

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Annual Workshop on Computational Learning Theory archive
Proceedings of the ninth annual conference on Computational learning theory table of contents

Desenzano del Garda, Italy

Pages: 110 - 121

Year of Publication: 1996

ISBN:0-89791-811-8

Author

V. G. Vovk

Department of Computer Science, Royal Holloway, University of London Egham, Surrey TW20 0EX, UK

Sponsors

Univ degli Studi de Milano : Universite degli Studi de Milano
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence

Publisher

ACM New York, NY, USA

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	R. Engelking, "General Topology". Warsaw: Pafistwowe Wydawnictwo Naukowe (1977)
	2	D. Geiger, T. Verma, and J. Pearl, Identifying independence in Bayesian networks. Networks 20, 507- 534 (1990)
	3	J. Pearl, Causal diagrams for empirical research (with discussion). Biometrika 82,669-710 (1995)
	4	J. Pearl, Causal diagrams for empirical research (with appendix on structural equations and counterfactuals). Technical Report R-218-B-L, Cognitive Systems Laboratory, Computer Science Department, University of California, Los Angeles, July 1995. Expanded version of {3}
	5	A. N. Shiryayev, "Probability". New York: Springer (1984)
	6	V. G. Vovk, Another semantics for Pearl's action calculus. In: "Computational Learning and Probabilistic Reasoning" (ed. by A. Gammerman), Chapter 7 (pp. 127-146). New York: Wiley (1995)

INDEX TERMS

Primary Classification:
I. Computing Methodologies
I.2 ARTIFICIAL INTELLIGENCE

General Terms:
Design, Performance, Theory, Verification

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