Memory intensive AND/OR search for combinatorial optimization in graphical models

Artificial Intelligence

Volumn 173, Issue 16-17, 2009, Pages 1492-1524

  Marinescu, Radu ^a   Dechter, Rina ^b  

^a UNIVERSITY COLLEGE CORK   (Ireland)

^b 5226 Donald Bren Hall   (United States)

Author keywords

AND OR search; Bayesian networks; Constraint networks; Constraint optimization; Decomposition; Graphical models; Search

Indexed keywords

AND/OR SEARCH; CONSTRAINT NETWORKS; CONSTRAINT OPTIMIZATION; GRAPHICAL MODELS; SEARCH;

COMBINATORIAL OPTIMIZATION; CONSTRAINED OPTIMIZATION; DISTRIBUTED PARAMETER NETWORKS; GRAPHIC METHODS; INFERENCE ENGINES; INTELLIGENT NETWORKS; LEARNING ALGORITHMS; SPEECH RECOGNITION;

BAYESIAN NETWORKS;

EID: 69249213463     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artint.2009.07.004     Document Type: Article

References (50)

1. Marinescu R., and Dechter R. AND/OR branch-and-bound search for combinatorial optimization in graphical models. Artificial Intelligence 173 16-17 (2009) 1457-1491 this issue
2. R. Marinescu, R. Dechter, AND/OR branch-and-bound for graphical models, in: International Joint Conference on Artificial Intelligence (IJCAI), 2005, pp. 224-229
3. R. Marinescu, R. Dechter, Dynamic orderings for AND/OR branch-and-bound search in graphical models, in: European Conference on Artificial Intelligence (ECAI), 2006, pp. 138-142
4. Dechter R. Enhancement schemes for constraint processing: Backjumping, learning and cutset decomposition. Artificial Intelligence 41 3 (1990) 273-312
5. R. Bayardo, D. Miranker, On the space-time trade-off in solving constraint satisfaction problems, in: International Joint Conference on Artificial Intelligence (IJCAI), 1995, pp. 558-562
6. Darwiche A. Recursive conditioning. Artificial Intelligence 126 1-2 (2001) 5-41
7. F. Bacchus, S. Dalmao, T. Pittasi, Value elimination: Bayesian inference via backtracking search, in: Uncertainty in Artificial Intelligence (UAI), 2003, pp. 20-28
8. P. Jegou, C. Terrioux, Decomposition and good recording for solving Max-CSPs, in: European Conference on Artificial Intelligence (ECAI), 2004, pp. 196-200
9. *. Journal of the ACM 32 3 (1985) 505-536
10. Kask K., and Dechter R. A general scheme for automatic generation of search heuristics from specification dependencies. Artificial Intelligence 129 1-2 (2001) 91-131
11. Dechter R., and Rish I. Mini-buckets: A general scheme for approximating inference. Journal of the ACM 50 2 (2003) 107-153
12. Pearl J. Probabilistic Reasoning in Intelligent Systems (1988), Morgan Kaufmann
13. Bistarelli S., Montanari U., and Rossi F. Semiring based constraint solving and optimization. Journal of the ACM 44 2 (1997) 309-315
14. R. Marinescu, R. Dechter, Memory intensive branch-and-bound search for graphical models, in: National Conference on Artificial Intelligence (AAAI), 2006
15. R. Marinescu, R. Dechter, Best-first AND/OR search for graphical models, in: National Conference on Artificial Intelligence (AAAI), 2007, pp. 1171-1176
16. R. Marinescu, R. Dechter, Best-first AND/OR search for most probable explanations, in: Uncertainty in Artificial Intelligence (UAI), 2007
17. Kask K., Dechter R., Larrosa J., and Dechter A. Unifying cluster-tree decompositions for reasoning in graphical models. Artificial Intelligence 166 1-2 (2005) 165-193
18. Dechter R., and Mateescu R. AND/OR search spaces for graphical models. Artificial Intelligence 171 1 (2007) 73-106
19. Nilsson N.J. Principles of Artificial Intelligence (1980), Tioga
20. E. Freuder, M. Quinn, Taking advantage of stable sets of variables in constraint satisfaction problems, in: International Joint Conference on Artificial Intelligence (IJCAI), 1985, pp. 1076-1078
21. Pearl J. Heuristics: Intelligent Search Strategies for Computer Problem Solving (1984), Addison-Wesley
22. Kanal L., and Kumar V. Search in Artificial Intelligence (1988), Springer-Verlag
23. R. Mateescu, R. Dechter, AND/OR cutset conditioning, in: International Joint Conference on Artificial Intelligence (IJCAI), 2005, pp. 230-235
24. A. Martelli, U. Montanari, Additive AND/OR graphs, in: International Joint Conference on Artificial Intelligence (IJCAI), 1973, pp. 1-11
25. Dechter R. Bucket elimination: A unifying framework for reasoning. Artificial Intelligence 113 1-2 (1999) 41-85
26. R. Marinescu, AND/OR search strategies for combinatorial optimization in graphical models, PhD thesis, University of California, Irvine, 2008
27. R. Marinescu, R. Dechter, Memory intensive AND/OR search for combinatorial optimization in graphical models, Technical report, University of California, Irvine, 2008
28. S. de Givry, F. Heras, J. Larrosa, M. Zytnicki, Existential arc consistency: Getting closer to full arc consistency in weighted CSPs, in: International Joint Conference in Artificial Intelligence (IJCAI), 2005, pp. 84-89
29. S. de Givry, T. Schiex, G. Verfaillie, Exploiting tree decomposition and soft local consistency in weighted CSP, in: National Conference on Artificial Intelligence (AAAI), 2006
30. T. Sang, P. Beame, H. Kautz, Solving Bayesian networks by weighted model counting, in: National Conference of Artificial Intelligence (AAAI), 2005, pp. 475-482
31. Ott J. Analysis of Human Genetic Linkage (1999), The Johns Hopkins University Press
32. M. Fishelson, D. Geiger, Exact genetic linkage computations for general pedigrees, in: International Conference on Intelligent Systems for Molecular Biology (ISMB), 2002, pp. 189-198
33. Fishelson M., Dovgolevsky N., and Geiger D. Maximum likelihood haplotyping for general pedigrees. Human Heredity 59 1 (2005) 41-60
34. J. Park, Using weighted Max-SAT engines to solve MPE, in: National Conference of Artificial Intelligence (AAAI), 2002, pp. 682-687
35. F. Hutter, H. Hoos, T. Stutzle, Efficient stochastic local search for MPE solving, in: International Joint Conference on Artificial Intelligence (IJCAI), 2005, pp. 169-174
36. C. Voudouris, Guided local search for combinatorial optimization problems. Technical report, PhD thesis, University of Essex, 1997
37. Mills P., and Tsang E. Guided local search for solving SAT and weighted MAX-SAT problems. Journal of Automated Reasoning (JAR) 24 1-2 (2000) 205-223
38. R. Dechter, D. Larkin, Hybrid processing of beliefs and constraints, in: Uncertainty in Artificial Intelligence (UAI), 2001, pp. 112-119
39. D. Larkin, R. Dechter, Bayesian inference in the presence of determinism, in: Artificial Intelligence and Statistics (AISTAT), 2003
40. D. Allen, A. Darwiche, New advances in inference using recursive conditioning, in: Uncertainty in Artificial Intelligence (UAI), 2003, pp. 2-10
41. R. Dechter, R. Mateescu, Mixtures of deterministic-probabilistic networks, in: Uncertainty in Artificial Intelligence (UAI), 2004, pp. 120-129
42. Rish I., and Dechter R. Resolution vs. search: Two strategies for SAT. Journal of Automated Reasoning 24 1-2 (2000) 225-275
43. T. Walsh, SAT vs CSP, in: Principles and Practice of Constraint Programming (CP), 2000, pp. 441-456
44. M. Moskewicz, C. Madigan, Y. Zhao, L. Zhang, S. Malik, Chaff: Engineering an efficient SAT solver, in: Design Automation Conference (DAC), 2001
45. Bensana E., Lemaitre M., and Verfaillie G. Earth observation satellite management. Constraints 4 3 (1999) 293-299
46. Chavira M., Darwiche A., and Jaeger M. Compiling relational Bayesian networks for exact inference. International Journal of Approximate Reasoning 42 1-2 (2006) 4-20
47. Chakrabati P., Ghose S., Acharya A., and de Sarkar S. Heuristic search in restricted memory. Artificial Intelligence 41 2 (1989) 197-221
48. Korf R. Linear-space best-first search. Artificial Intelligence 62 1 (1993) 41-78
49. R. Zhou, E. Hansen, Structured duplicate detection in external-memory graph search, in: National Conference on Artificial Intelligence (AAAI-04), 2004, pp. 683-689
50. R. Zhou, E. Hansen, External-memory pattern databases using structured duplicate detection, in: National Conference on Artificial Intelligence (AAAI-05), 2005, pp. 1398-1405