Boosting learning and inference in Markov logic through metaheuristics

Applied Intelligence

Volumn 34, Issue 2, 2011, Pages 279-298

  Biba, Marenglen ^a   Ferilli, Stefano ^a   Esposito, Floriana ^a  

^a UNIVERSITY OF BARI   (Italy)

Author keywords

Inference; Markov logic; Metaheuristics; Probabilistic graphical models; Statistical relational learning; Structure learning

Indexed keywords

INFERENCE; MARKOV LOGIC; META HEURISTICS; PROBABILISTIC GRAPHICAL MODELS; STATISTICAL RELATIONAL LEARNING; STRUCTURE LEARNING;

EXPERIMENTS; HEURISTIC ALGORITHMS; HEURISTIC METHODS; LEARNING ALGORITHMS; PROBABILISTIC LOGICS; SAMPLING; SIMULATED ANNEALING; TABU SEARCH;

INFERENCE ENGINES;

EID: 79956157449     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-009-0195-6     Document Type: Article

References (60)

1. Bacchus F (1990) Representing and reasoning with probabilistic knowledge. MIT Press, Cambridge
2. Battiti R, Protasi M (1997) Reactive search, a history-based heuristic for max-sat. ACM J Exp Algorithmics 2
3. Besag J (1975) Statistical analysis of non-lattice data. Statistician 24:179-195
4. Biba M, Ferilli S, Esposito F (2008) Discriminative structure learning of Markov logic networks. In: Inductive logic programming, 18th international conference, ILP 2008, Prague, Czech Republic, September 10-12, 2008, Proceedings. LNCS, vol 5194. Springer, Berlin, pp 59-76
5. Damien P, Wakefield J, Walker S (1999) Gibbs sampling for Bayesian non-conjugate and hierarchical models by auxiliary variables. J R Stat Soc B 61:2
6. Davis J, Goadrich M (2006) The relationship between precisionrecall and roc curves. In: Proc. 23rd ICML, pp 233-240
7. De Raedt L, Dehaspe L (1997) Clausal discovery. Mach Learn 26:99-146 (Pubitemid 127507163)
8. Dehaspe L (1997) Maximum entropy modeling with clausal constraints. In: Proc. of 17th int'l workshop on inductive logic programming. LNCS, vol 1297. Springer, Berlin, pp 109-124 (Pubitemid 127124382)
9. Della Pietra S, Della Pietra V, Laferty J (1997) Inducing features of random fields. IEEE Trans Pattern Anal Mach Intell 19:380-392
10. Fonlupt C, Robilliard D, Preux P, Talbi E-G (1999) Fitness landscape and performance of meta-heuristics. In: Voss S, Martello S, Osman IH, Roucairol C (eds) Meta-heuristics: Advances and trends in local search paradigms for optimization. Kluwer Academic, Boston, pp 257-268
11. Friedman N, Getoor L, Koller D, Pfeffer A (1999) Learning probabilistic relational models. In: Proc. 16th int'l joint conf. on AI (IJCAI). Kaufmann, San Mateo, pp 1300-1307
12. Garey MR, Johnson DS (1979) Computers and intractability: A guide to the theory of NP-completeness. Freeman, San Francisco
13. Genesereth MR, Nilsson NJ (1987) Logical foundations of artificial intelligence. Kaufmann, San Mateo
14. Getoor L, Taskar B (2007) Introduction to statistical relational learning. MIT Press, Cambridge
15. Gilks WR, Richardson S, Spiegelhalter DJ (1996) Markov chain Monte Carlo in practice. Chapman & Hall, London
16. Glover F, Laguna M (1997) Tabu search. Kluwer Academic, Boston
17. Halpern J (1990) An analysis of first-order logics of probability. Artif Intell 46:311-350
18. Hansen P, Jaumard B (1990) Algorithms for the maximum satisfiability problem. Computing 44:279-303 (Pubitemid 20722282)
19. Hoos HH, Stutzle T (2005) Stochastic local search: Foundations and applications. Kaufmann, San Francisco
20. Huynh TN, Mooney RJ (2008) Discriminative structure and parameter learning for Markov logic networks. In: Proc. of the 25th international conference on machine learning (ICML)
21. Kautz H, Selman B, Jiang Y (1997) A general stochastic approach to solving problems with hard and soft constraints. In: The satisfiability problem: Theory and applications. AMS, Providence
22. Kersting K, De Raedt L (2001) Towards combining inductive logic programming with Bayesian networks. In: Proc. 11th int'l conf. on inductive logic programming. Springer, Berlin, pp 118-131 (Pubitemid 33332602)
23. Kok S, Domingos P (2005) Learning the structure of Markov logic networks. In: Proc. 22nd int'l conf. on machine learning, pp 441-448 (Pubitemid 43183364)
24. Kok S, Singla P, Richardson M, Domingos P (2005) The alchemy system for statistical relational ai. Technical Report, Department of CSE-UW, Seattle, WA, http://alchemy.cs.washington.edu/
25. Landwehr N, Kersting K, De Raedt L (2005) nfoil: Integrating naive Bayes and foil. In: Proc. 20th nat'l conf. on artificial intelligence. AAAI Press, Menlo Park, pp 795-800 (Pubitemid 43006706)
26. Landwehr N, Passerini A, De Raedt L, Frasconi P (2006) kfoil: Learning simple relational kernels. In: Proc. 21st nat'l conf. on artificial intelligence. AAAI Press, Menlo Park
27. Landwehr N, Kersting K, De Raedt L (2007) Integrating naive Bayes and foil. J Mach Learn Res 481-507
28. Lavrac N, Dzeroski S (1994) Inductive logic programming: Techniques and applications. Ellis Horwood, Chichester
29. Liu DC, Nocedal J (1989) On the limited memory bfgs method for large scale optimization. Math Program 45:503-528 (Pubitemid 20660315)
30. Loureno HR, Martin O, Stutzle T (2002) Iterated local search. In: Glover F, Kochenberger G (eds) Handbook of metaheuristics. Kluwer Academic, Norwell, pp 321-353
31. Lowd D, Domingos P (2007) Efficient weight learning forMarkov logic networks. In: Proc. of the 11th PKDD. Springer, Berlin, pp 200-211
32. McCallum A (2003) Efficiently inducing features of conditional random fields. In: Proc. UAI-03, pp 403-410
33. Mezard M, Parisi G, Virasoro MA (1987) Spin-glass theory and beyond. In: Lecture notes in physics, vol 9. World Scientific, Singapore
34. Mihalkova L, Mooney RJ (2007) Bottom-up learning of Markov logic network structure. In: Proc. 24th int'l conf. on machine learning, pp 625-632
35. Mihalkova L, Richardson M (2008) Speeding up inference in statistical relational learning by clustering similar query literals. Technical Report, Microsoft Research Technical ReportMSR-TR-2008-72, May 2008
36. Mills P, Tsang E (2000) Guided local search for solving sat and weighted max-sat problems. In: Gent IP, van Maaren H, Walsh T (eds) SAT2000-Highlights of satisfiability research in the year 2000. IOS Press, pp 89-106
37. Neville J, Jensen D (2004) Dependency networks for relational data. In: Proc. 4th IEEE int'l conf. on data mining. IEEE Comput Soc, Los Alamitos, pp 170-177 (Pubitemid 40731028)
38. Nilsson N (1986) Probabilistic logic. Artif Intell 28:71-87
39. Park JD (2005) Using weighted max-sat engines to solve mpe. In: Proc. of AAAI, pp 682-687
40. Pereira J, Saraiva JT, PoncedeLeao MT (1999) Identification of operation strategies of distribution networks using a simulated annealing approach. In: IEEE Power tech conference, Budapest
41. Poon H, Domingos P (2006) Sound and efficient inference with probabilistic and deterministic dependencies. In: Proc. 21st nat'l conf. on AI (AAAI). AAAI Press, Menlo Park, pp 458-463 (Pubitemid 44705326)
42. Poon H, Domingos P, Sumner M (2008) A general method for reducing the complexity of relational inference and its application to mcmc. In: Proc. 23rd nat'l conf. on artificial intelligence. AAAI Press, Chicago
43. Quinlan JR (1990) Learning logical definitions from relations. Mach Learn 5:239-266
44. Richardson M, Domingos P (2006) Markov logic networks. Mach Learn 62:107-236
45. Riedel S (2008) Improving the accuracy and efficiency of map inference for Markov logic. In: Proceedings of UAI
46. Roth D (1996) On the hardness of approximate reasoning. Artif Intell 82:273-302 (Pubitemid 126374156)
47. Selman B, Kautz H, Cohen B (1996) Local search strategies for satisfiability testing. In: Cliques, coloring, and satisfiability: Second DIMACS implementation challenge. Am Math Soc, Providence, pp 521-532
48. Sha F, Pereira F (2003) Shallow parsing with conditional random fields. In: Proc. HLT-NAACL-03, pp 134-141
49. Shang Y,Wah B (1997) Discrete lagrangian-based search for solving max-sat problems. In: Proc. of IJCAI. Kaufmann, San Francisco, pp 378-383
50. Singla P, Domingos P (2005) Discriminative training of Markov logic networks. In: Proc. 20th nat'l conf. on AI, (AAAI). AAAI Press, Menlo Park, pp 868-873 (Pubitemid 43006718)
51. Singla P, Domingos P (2006) Entity resolution with Markov logic. In: Proc. ICDM-2006. IEEE Comput Soc, Los Alamitos, pp 572-582 (Pubitemid 47485836)
52. Singla P, Domingos P (2007) Markov logic in infinite domains. In: Proc. 23rd UAI. AUAI Press, pp 368-375
53. Smyth K, Hoos H, Stützle T (2003) Iterated robust tabu search for max-sat. In: Canadian conference on AI, pp 129-144 (Pubitemid 36796053)
54. Taillard ED (1991) Robust taboo search for the quadratic assignment problem. Parallel Comput 17:443-455
55. Wei W, Erenrich J, Selman B (2004) Towards efficient sampling: Exploiting random walk strategies. In: Proc. 19th nat'l conf. on AI, (AAAI)
56. Wellman JS, Breese M, Goldman RP (1992) From knowledge bases to decision models. Knowl Eng Rev 7
57. Wu Z, Wah BW (1999) Trap escaping strategies in discrete lagrangian methods for solving hard satisfiability and maximum satisfiability problems. In: Proc. of AAAI. MIT Press, Cambridge, pp 673-678
58. Yagiura M, Ibaraki T (2001) Efficient 2 and 3-flip neighborhood search algorithms for the max sat:experimental evaluation. J Heuristics 7(5):423-442 (Pubitemid 32886778)
59. Zafiropoulos EP, Dialynas EN (2004) Reliability and cost optimization of electronic devices considering the component failure rate uncertainty. Reliab Eng Syst Saf 84(3):271-284
60. Zelezny F, Srinivasan A, Page D (2006) Randomised restarted search in ilp. Mach Learn 64(1-3):183-208 (Pubitemid 44320251)