Topological parameters for time-space tradeoff

Artificial Intelligence

Volumn 125, Issue 1-2, 2001, Pages 93-118

  Dechter, Rina ^a   El Fattah, Yousri ^b  

^a Ringgold Standard Institution Informatics   (United States)

^b ROCKWELL SCIENCE CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; AUTOMATION; CONSTRAINT THEORY; INFERENCE ENGINES; PROBABILITY DISTRIBUTIONS; PROBLEM SOLVING; TOPOLOGY; TREES (MATHEMATICS);

BAYESIAN NETWORKS; HYBRID ALGORITHMS; TOPOLOGICAL PARAMETERS;

ARTIFICIAL INTELLIGENCE;

EID: 0035121258     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0004-3702(00)00050-3     Document Type: Article

References (40)

1. Arnborg S., Proskourowski A. Linear time algorithms for NP-hard problems restricted to partial k -trees. Discrete Appl. Math. 23:1989;11-24.
2. Arnborg S.A. Efficient algorithms for combinatorial problems on graphs with bounded decomposability - A survey. BIT. 25:1985;2-23.
3. Bacchus F., van Run P. Dynamic variable ordering in CSPS. Principles and Practice of Constraints Programming (CP'95), Cassis, France. Lecture Notes in Comput. Sci. 976:1995;258-277 Springer, Berlin.
4. Becker A., Geiger D. A sufficiently fast algorithm for finding close to optimal junction trees. Proc. Uncertainty in AI (UAI-96), Portland, OR. 1996;81-89.
5. Bertele U., Brioschi F. Nonserial Dynamic Programming. 1972;Academic Press, New York.
6. Brglez F., Fujiwara H. A neutral netlist of 10 combinatorial benchmark circuits and a target translator in Fortran. Proc. IEEE International Symposium on Circuits and Systems. 1996.
7. Darwiche A. Conditioning algorithms for exact and approximate inference in causal networks. Proc. Uncertainty in Artificial Intelligence (UAI-95), Montreal, Quebec. 1995;99-107.
8. Dechter R. Enhancement schemes for constraint processing: Backjumping, learning and cutset decomposition. Artificial Intelligence. 41:1990;273-312.
9. Dechter R. Constraints networks. Encyclopedia of Artificial Intelligence. 2nd edn. 1992;276-285.
10. Dechter R. Bucket elimination: A unifying framework for probabilistic inference algorithms. Proc. Uncertainty in Artificial Intelligence (UAI-96), Portland, OR. 1996;211-219.
11. Dechter R. Bucket elimination: A unifying framework for reasoning. Artificial Intelligence. 113:1999;41-85.
12. Dechter R., El Fattah Y. Topological parameters for time-space tradeoff, Technical Report. 1999;Information and Computer Science, University of California, Irvine, CA.
13. Dechter R., Pearl J. Network-based heuristics for constraint satisfaction problems. Artificial Intelligence. 34:1987;1-38.
14. Dechter R., Pearl J. Tree clustering for constraint networks. Artificial Intelligence. 38:1989;353-366.
15. Diez F.J. Local conditioning in Bayesian networks. Artificial Intelligence. 87:1996;1-20.
16. El Fattah Y., Dechter R. Diagnosing tree-decomposable circuits. Proc. IJCAI-95, Montreal, Quebec. 1995;1742-1748.
17. Freuder E.C. A sufficient condition for backtrack-free search. J. ACM. 29:(1):1982;24-32.
18. Geffner H., Pearl J. An improved constraint propagation algorithm for diagnosis. Proc. IJCAI-87, Milan, Italy. 1987;1105-1111.
19. Cooper G.F., Suermondt H.J., Heckerman D.E. A combination of cutset conditioning with clique-tree propagation in the path-finder system. Proc. Uncertainty in Artificial Intelligence (UAI-91), Los Angeles, CA. 1991;245-253.
20. Howard R.A., Matheson J.E. Influence diagrams. Howard R.A., Matheson J.E. The Principles and Applications of Decision Analysis, Vol. 2. 1984;Strategic Decision Group, Menlo Park, CA.
21. Jegou P. Cyclic clustering: A compromise between tree-clustering and the cycle-cutset method for improving search efficiency. Proc. European Conference on AI (ECAI-90), Stockholm, Sweden. 1990;369-371.
22. Jensen F.V., Lauritzen S.L., Olesen K.G. Bayesian updating in causal probabilistic networks by local computation. Computational Statistics Quarterly. 4:1990;269-282.
23. Kjærulff U. Triangulation of graphs - Algorithms giving small total state space. Technical Report 90-09. 1990;Department of Mathematics and Computer Science, Aalborg University, Denmark.
24. Kjærulff U. Nested junction trees. Proc. Uncertainty in Artificial Intelligence (UAI-97), Providence, RI. 1997;294-301.
25. Krantz D.H., Luce R.D., Suppes P., Tversky A. Foundations of Measurements. 1976;Academic Press, New York.
26. Lauritzen S.L., Spiegelhalter D.J. Local computation with probabilities on graphical structures and their application to expert systems. J. Roy. Statist. Soc. B. 50:(2):1988;157-224.
27. Mackworth A.K., Freuder E.C. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence. 25:1985;65-74.
28. Pearl J. Fusion propagation and structuring in belief networks. Artificial Intelligence. 19:(3):1986;241-248.
29. Pearl J. Probabilistic Reasoning in Intelligent Systems. 1988;Morgan Kaufmann, San Mateo, CA.
30. Peot M.A., Shachter R.D. Fusion and propagation with multiple observations in belief networks. Artificial Intelligence. 48:1991;299-318.
31. Anderson S.K., Shachter R.D., Solovitz P. Global conditioning for probabilistic inference in belief networks. Proc. Uncertainty in Artificial Intelligence (UAI-94), Seattle, WA. 1994;514-522.
32. Dechter A., Dechter R., Pearl J. Optimization in constraint networks. Influence Diagrams, Belief Nets and Decision Analysis. 1990;411-425 Wiley, New York.
33. Rish I., Dechter R. To guess or to think? Hybrid algorithms for SAT. Proc. Principles of Constraint Programming (CP-96). 1996;555-556.
34. Rish I., Dechter R. Resolution vs. SAT: Two approaches to SAT. J. Approx. Reason. (Special Issue on SAT). 24:2000;225-275.
35. Corneil D.G., Arnborg S.A., Proskourowski A. Complexity of finding embeddings in a k -tree. SIAM J. Discrete Math. 8:1987;277-284.
36. Schmidt T., Shenoy P.P. Some improvements to the Shenoy-Shafer and Hugin architectures for computing marginals. Artificial Intelligence. 102:1998;323-333.
37. Shafer G. Probabilistic Expert Systems. 1996;SIAM, Philadelphia, PA.
38. Shafer G.R., Shenoy P.P. Probability propagation. Ann. Math. Artificial Intelligence. 2:1990;327-352.
39. Shoiket K., Geiger D. A practical algorithm for finding optimal triangulations. Proc. AAAI-97, Providence, RI. 1997;185-190.
40. Srinivas S. A probabilistic approach to hierarchical model-based diagnosis. Working Notes of the Fifth International Workshop on Principles of Diagnosis, New Paltz, NY. 1994;305-311.