Constraint Programming Bibliography

Surveys and books

1. J. Cohen. Constraint logic programming languages. Comm. ACM, 33:52-68, 1990.
2. R. Dechter. Constraint networks. In Encyclopedia of Artificial Intelligence, Second Edition, pages 276-285. John Wiley & Sons, 1992.
3. V. Kumar. Algorithms for constraint-satisfaction problems: A survey. AI Magazine, 13:32-44, 1992.
4. A. K. Mackworth. Constraint satisfaction. In S. C. Shapiro, editor, Encyclopedia of Artificial Intelligence. John Wiley & Sons, 1987.
5. B. A. Nadel. Some applications of the constraint satisfaction problem. In AAAI-90 Workshop on Constraint Directed Reasoning Working Notes, Boston, Mass., 1990.
6. E. Tsang. Foundations of Constraint Satisfaction. Academic Press, 1993.
7. P. Van Hentenryck. Constraint Satisfaction in Logic Programming. MIT Press, 1989.

Modeling

8. A. Dechter and R. Dechter. Removing redundancies in constraint networks. In Proc. of the Sixth National Conference on Artificial Intelligence, pages 105-109, Seattle, Wash., 1987.
9. L. Getoor, G. Ottosson, M. Fromherz, and B. Carlson. Effective redundant constraint for online scheduling. In Proceedings of the Fourteenth National Conference on Artificial Intelligence, pages 302-307.
10. B. Smith, S. C. Brailsford, P. M. Hubbard and H. P. Williams. The progressive party problem: Integer linear programming and Constraint Programming compared. Constraints, 1:119-138, 1996.
11. R. Dechter. Decomposing a relation into a tree of binary relations. J. of Computer and System Sciences, 41, 1990.
12. R. Dechter. On the expressiveness of networks with hidden variables. In Proc. of the Eighth National Conference on Artificial Intelligence, pages 556-562, Boston, Mass., 1990.
13. P. Van Hentenryck and J.-P. Carillon. Generality versus specificity: An experience with AI and OR techniques. In Proceedings of the Seventh National Conference on Artificial Intelligence, pages 660-664, 1988.
14. I. Meiri, R. Dechter, and J. Pearl. Tree decomposition with applications to constraint processing. In Proc. of the Eighth National Conference on Artificial Intelligence, pages 10-16, Boston, Mass., 1990.
15. B. A. Nadel. Representation selection for constraint satisfaction: A case study using $n$-queens. IEEE Expert, 5:16-23, 1990.
16. F. Rossi, C. Petrie, and V. Dhar. On the equivalence of constraint satisfaction problems. In Proc. of the 9th European Conference on Artificial Intelligence, pages 550-556, Stockholm, Sweden, 1990.
17. J. E. Borrett. Formulation selection for Constraint Satisfaction Problems: A Heuristic Approach. PhD thesis, University of Essex, United Kingdom, 1998.
18. R. Weigel, C. Bliek, and B. Faltings. On reformulation of constraint satisfaction problems. In Proceedings of the 13^th European Conference on Artificial Intelligence. Brighton, United Kingdom, 1998.

Consistency algorithms

19. C. Bessiere. Arc-consistency and arc-consistency again. Artificial Intelligence, 65:179-190, 1994.
20. C. Bessiere, E. C. Freuder and J.-C. Regin. Using inference to reduce arc consistency computation. In Proc. of the Fourteenth International Joint Conference on Artificial Intelligence, pages 592-599, Montreal, Quebec, 1995.
21. M. C. Cooper. An optimal k-consistency algorithm. Artificial Intelligence, 41:89-95, 1989.
22. E. Davis. Constraint propagation with interval labels. Artificial Intelligence, 32:281-331, 1987.
23. R. Dechter and P. van Beek. Local and Global Relational Consistency. Theoretical Computer Science , 173:283-308, 1997.
24. B. Faltings. Arc-consistency for continuous variables. Artificial Intelligence, 65:363-376, 1994.
25. C.-C. Han and C.-H. Lee. Comments on Mohr and Henderson's path consistency algorithm. Artificial Intelligence, 36:125-130, 1988.
26. D. Haroud and B. Faltings. Global consistency for continuous constraints. In Proceedings of the 11th European Conference on Artificial Intelligence, pages 115-119, Amsterdam, 1994.
27. P. Jegou. On the consistency of general constraint satisfaction problems. In Proceedings of the Eleventh National Conference on Artificial Intelligence, pages 114-119, Washington, DC, 1993.
28. S. Kasif. On the parallel complexity of discrete relaxation in constraint satisfaction networks. Artificial Intelligence, 45:275-286, 1990.
29. S. Kasif and A. L. Delcher. Local consistency in parallel constraint satisfaction networks. Artificial Intelligence, 69:307-328, 1994.
30. B. Liu. Increasing functional contraints need to be checked only once. In Proc. of the Fourteenth International Joint Conference on Artificial Intelligence, pages 586-591, Montreal, Quebec, 1995.
31. A. K. Mackworth. Consistency in networks of relations. Artificial Intelligence, 8:99-118, 1977.
32. A. K. Mackworth and E. C. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, 25:65-74, 1985.
33. A. K. Mackworth, J. A. Mulder, and W. S. Havens. Hierarchical arc consistency: Exploiting structured domains in constraint satisfaction problems. Computational Intelligence, 1:118-126, 1985.
34. R. Mohr and T. C. Henderson. Arc and path consistency revisited. Artificial Intelligence, 28:225-233, 1986.
35. U. Montanari. Networks of constraints: Fundamental properties and applications to picture processing. Inform. Sci., 7:95-132, 1974.
36. M. Perlin. Arc consistency for factorable relations. Artificial Intelligence, 53:329-342, 1992.
37. J.-C. Regin. A filtering algorithm for constraints of difference in csp. In Proceedings of the Twelfth National Conference on Artificial Intelligence, Seattle, Wash., 1994.
38. P. Van Hentenryck, Y. Deville, and C.-M. Teng. A generic arc consistency algorithm and its specializations. Artificial Intelligence, 57:291-321, 1992.
39. R. J. Wallace and E. C. Freuder. Ordering heuristics for arc consistency algorithms. In Proc. of the Ninth Canadian Conference on Artificial Intelligence, pages 163-169, Vancouver, B.C., 1992.

Backtracking algorithms

40. A. B. Baker. The hazards of fancy backtracking. In Proceedings of the Twelfth National Conference on Artificial Intelligence, Seattle, Wash., 1994.
41. R. J. Bayardo Jr. and D. P. Miranker. An optimal backtrack algorithm for tree-structured constraint satisfaction problems. Artificial Intelligence, 71:159-182, 1994.
42. B. W. Benson Jr. and E. C. Freuder. Interchangeability preprocessing can improve forward checking search. In Proceedings of the 10th European Conference on Artificial Intelligence, pages 28-30, Vienna, 1992.
43. J. R. Bitner and E. M. Reingold. Backtrack programming techniques. Comm. ACM, 18:651-655, 1975.
44. D. Brelaz. New methods to color the vertices of a graph. Comm. ACM, 22:251-256, 1979.
45. C. A. Brown and P. W. Purdom, Jr. An average time analysis of backtracking. SIAM J. Comput., 10:583-593, 1981.
46. R. Dechter. Enhancement schemes for constraint processing: Backjumping, learning, and cutset decomposition. Artificial Intelligence, 41:273-312, 1990.
47. R. Dechter and I. Meiri. Experimental evaluation of preprocessing techniques in constraint satisfaction problems. In Proc. of the Eleventh International Joint Conference on Artificial Intelligence, pages 271-277, Detroit, Mich., 1989.
48. R. Dechter and J. Pearl. Network-based heuristics for constraint satisfaction problems. Artificial Intelligence, 34:1-38, 1988.
49. R. Dechter and J. Pearl. Tree clustering for constraint networks. Artificial Intelligence, 38:353-366, 1989.
50. D. Frost and R. Dechter. Dead-end driven learning. In Proceedings of the Twelfth National Conference on Artificial Intelligence, pages 301-306, Seattle, Wash., 1994.
51. D. Frost and R. Dechter. In search of the best search: An empirical evaluation. In Proceedings of the Twelfth National Conference on Artificial Intelligence, pages 294-300, Seattle, Wash., 1994.
52. D. Frost and R. Dechter. Look-ahead value ordering for constraint satisfaction problems. In Proc. of the Fourteenth International Joint Conference on Artificial Intelligence, pages 572-578, Montreal, Quebec, 1995.
53. J. Gaschnig. A general backtracking algorithm that eliminates most redundant tests. In Proceedings of the Fifth International Joint Conference on Artificial Intelligence, page 457, Cambridge, Mass., 1977.
54. J. Gaschnig. Experimental case studies of backtrack vs. waltz-type vs. new algorithms for satisficing assignment problems. In Proc. of the Second Canadian Conference on Artificial Intelligence, pages 268-277, Toronto, Ont., 1978.
55. P. A. Geelen. Dual viewpoint heuristics for binary constraint satisfaction problems. In Proceedings of the 10th European Conference on Artificial Intelligence, pages 31-35, Vienna, 1992.
56. M. L. Ginsberg. Dynamic backtracking. J. of Artificial Intelligence Research, 1:25-46, 1993.
57. M. L. Ginsberg, M. Frank, M. P. Halpin, and M. C. Torrance. Search lessons learned from crossword puzzles. In Proc. of the Eighth National Conference on Artificial Intelligence, pages 210-215, Boston, Mass., 1990.
58. S. Golomb and L. Baumert. Backtrack programming. J. ACM, 12:516-524, 1965.
59. R. M. Haralick and G. L. Elliott. Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14:263-313, 1980.
60. R. M. Karp and Y. Zhang. Randomized parallel algorithms for backtrack search and branch-and-bound computation. J. ACM, 40:765-789, 1993.
61. G. Kondrak and P. van Beek. A theoretical evaluation of selected backtracking algorithms. In Proc. of the Fourteenth International Joint Conference on Artificial Intelligence, pages 541-547, Montreal, Quebec, 1995.
62. D. E. Knuth. Estimating the efficiency of backtrack programs. Mathematics of Computation, 29:121-136, 1975.
63. J. J. McGregor. Relational consistency algorithms and their application in finding subgraph and graph isomorphisms. Inform. Sci., 19:229-250, 1979.
64. S. Minton. Integrating heuristics for constraint satisfaction problems: A case study. In Proc. of the Eleventh National Conference on Artificial Intelligence, pages 120-126, Washington, DC, 1993.
65. B. A. Nadel. Constraint satisfaction algorithms. Computational Intelligence, 5:188-224, 1989.
66. D. M. Nicol. Expected performance of $m$-solution backtracking. SIAM J. Comput., 17:114-127, 1988.
67. B. Nudel. Consistent-labeling problems and their algorithms: Expected-complexities and theory-based heuristics. Artificial Intelligence, 21:135-178, 1983.
68. P. Prosser. Domain filtering can degrade intelligent backtrackng search. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pages 262-267, 1993.
69. P. Prosser. Hybrid algorithms for the constraint satisfaction problem. Computational Intelligence, 9:268-299, 1993.
70. H. S. Stone and P. Sipala. The average complexity of depth-first search with backtracking and cutoff. IBM J. Res. and Develop., 30:242-258, 1986.
71. P. W. Purdom, Jr. Search rearrangement backtracking and polynomial average time. Artificial Intelligence, 21:117-133, 1983.

Synthesis algorithms

72. R. J. Bayardo Jr. and D. P. Miranker. On the space-time trade-off in solving constraint satisfaction problems. In Proc. of the Fourteenth International Joint Conference on Artificial Intelligence, pages 558-562, Montreal, Quebec, 1995.
73. J. de Kleer. A comparison of ATMS and CSP techniques. In Proc. of the Eleventh International Joint Conference on Artificial Intelligence, pages 290-296, Detroit, Mich., 1989.
74. E. C. Freuder. Synthesizing constraint expressions. Comm. ACM, 21:958-966, 1978.
75. H. S. Lee. Solving n-ary constraint labeling problems using incremental subnetwork consistency. Technical report, IBM T. J. Watson Research Center.
76. R. Seidel. A new method for solving constraint satisfaction problems. In Proc. of the Seventh International Joint Conference on Artificial Intelligence, pages 338-342, Vancouver, B.C., 1981.
77. E. Tsang. Chapter 9 of Foundations of Constraint Satisfaction. Academic Press, 1993.

Stochastic algorithms

78. H.-M. Adorf and M. D. Johnston. A discrete stochastic neural network algorithm for constraint satisfaction problems. In Proceedings of the International Joint Conference on Neural Networks, pages 17-21, San Diego, Calif., 1990.
79. B. Cha and K. Iwama. Performance test of local search algorithms using new types of random CNF formulas. In Proc. of the Fourteenth International Joint Conference on Artificial Intelligence, pages 304-311, Montreal, Quebec, 1995.
80. A. Davenport, E. Tsang, C. J. Wang, and K. Zhu. GENET: A connectionist architecture for solving constraint satisfaction problems by interative improvement. In Proc. of the Twelfth National Conference on Artificial Intelligence, pages 325-330, Washington, DC, 1994.
81. K. Kask and R. Dechter. GSAT and local consistency. In Proc. of the Fourteenth International Joint Conference on Artificial Intelligence, pages 616-623, Montreal, Quebec, 1995.
82. S. Minton, M. D. Johnston, A. B. Philips, and P. Laird. Solving large-scale constraint satisfaction and scheduling problems using a heuristic repair method. In Proc. of the Eighth National Conference on Artificial Intelligence, pages 17-24, Boston, Mass., 1990.
83. B. Selman, and H. A. Kautz. An empirical study of greedy local search for satisfiability testing. In Proc. of the Eleventh National Conference on Artificial Intelligence, pages 46-52, Washington, DC, 1993.
84. B. Selman, H. A. Kautz, and B. Cohen. Noise strategies for improving local search. In Proceedings of the Twelfth National Conference on Artificial Intelligence, Seattle, Wash., 1994.
85. B. Selman, H. Levesque, and D. Mitchell. A new method for solving hard satisfiability problems. In Proc. of the Tenth National Conference on Artificial Intelligence, pages 440-446, San Jose, Calif., 1992.
86. G. A. Tagliarini. Solving constraint satisfaction problems with neural networks. In Proceedings of the IEEE First International Conference on Neural Networks (ICNN), pages 741-747, 1987.
87. N. Yugami, Y. Ohta, and H. Hara. Improving repair-based constraint satisfaction methods by value propagation. In Proceedings of the Twelfth National Conference on Artificial Intelligence, Seattle, Wash., 1994.

Soft constraints

88. S. Bistarelli, U. Montanari and F. Rossi. Constraint solving over semirings. In Proc. of the Fourteenth International Joint Conference on Artificial Intelligence, pages 624-630, Montreal, Quebec, 1995.
89. R. Dechter, A. Dechter, and J. Pearl. Optimization in constraint networks. In Influence Diagrams, Belief Nets and Decision Analysis. John Wiley & Sons Ltd., 1990.
90. E. C. Freuder and R. J. Wallace. Partial constraint satisfaction. Artificial Intelligence, 58:21-70, 1992.

Easy classes of problems

91. M. C. Cooper, D. A. Cohen, and P. G. Jeavons. Characterising tractable constraints. Artificial Intelligence, 65:347-361, 1994.
92. R. Dechter. From local to global consistency. Artificial Intelligence, 55:87-107, 1992.
93. E. C. Freuder. A sufficient condition for backtrack-free search. J. ACM, 29:24-32, 1982.
94. E. C. Freuder. A sufficient condition for backtrack-bounded search. J. ACM, 32:755-761, 1985.
95. E. C. Freuder. Complexity of k-tree structured constraint satisfaction problems. In Proc. of the Eighth National Conference on Artificial Intelligence, pages 4-9, Boston, Mass., 1990.
96. L. M. Kirousis. Fast parallel constraint satisfaction. Artificial Intelligence, 64:147-160, 1993.
97. P. van Beek. On the minimality and decomposability of constraint networks. In Proc. of the Tenth National Conference on Artificial Intelligence, pages 447-452, San Jose, Calif., 1992.
98. P. van Beek and R. Dechter. Constraint tightness versus global consistency. In Proc. of the Fourth International Conference on Principles of Knowledge Representation and Reasoning, pages 572-582, Bonn, Germany, 1991.

Hard classes of problems

99. P. Cheeseman, B. Kanefsky, and W. M. Taylor. Where the really hard problems are. In Proc. of the Twelfth International Joint Conference on Artificial Intelligence, pages 331-337, Sydney, Australia, 1991.
100. T. Hogg and C. P. Williams. The hardest constraint problems: A double phase transition. Artificial Intelligence, 69:359-378, 1994.
101. D. Mitchell, B. Selman, and H. Levesque. Hard and easy distributions of SAT problems. In Proc. of the Tenth National Conference on Artificial Intelligence, pages 459-465, San Jose, Calif., 1992.
102. P. Prosser. Binary constraint satisfaction problems: some are harder than others. In Proceedings of the 11th European Conference on Artificial Intelligence, pages 95-99, Amsterdam, 1994.
103. B. M. Smith and S. A. Grant. Sparse constraint graphs and exceptionally hard problems. In Proc. of the Fourteenth International Joint Conference on Artificial Intelligence, pages 646-654, Montreal, Quebec, 1995.
104. T. Schiex and G. Verfaillie. Valued constraint satisfaction problems: hard and easy problems. In Proc. of the Fourteenth International Joint Conference on Artificial Intelligence, pages 631-639, Montreal, Quebec, 1995.
105. C. P. Williams, and T. Hogg. Using deep structure to locate hard problems. In Proc. of the Tenth National Conference on Artificial Intelligence, pages 472-477, San Jose, Calif., 1992.
106. C. P. Williams and T. Hogg. Exploiting the deep structure of constraint problems. Artificial Intelligence, 70:73-117, 1994.

Parallel and Distributed

107. R. Finkel and U. Manber. DIB--A distributed implementation of backtracking. ACM Transactions on Programming Languages and Systems, 9:235-256, 1987.
108. T. Hogg and C. P. Williams. Expected gains from parallelizing constraint solving for hard problems. In Proceedings of the Twelfth National Conference on Artificial Intelligence, pages 331-336, Seattle, Wash., 1994.
109. V. Kumar and V. N. Rao. Scalable parallel formulations of depth-first search. In V. Kumar, P. S. Gopalakrishnan, and L. Kanal, editors, Parallel Algorithms for Machine Intelligence and Vision, pages 1-41. Springer-Verlag, 1990.
110. Q. Y. Luo, P. G. Hendry, and J. T. Buchanan. A new algorithm for dynamic distributed constraint satisfaction problems. In Proceedings of the Fifth Florida Artificial Intelligence Research Symposium, pages 52-56, Ft. Lauderdale, Florida, 1992.
111. Y. Zhang and A. K. Mackworth. Parallel and distributed algorithms for finite constraint satisfaction problems. In Proceedings of the Third IEEE Symposium on Parallel and Distributed Processing, pages 394-397, Dallas, Texas, 1991.

Temporal reasoning

112. J. F. Allen. Maintaining knowledge about temporal intervals. Comm. ACM, 26:832-843, 1983.
113. R. Dechter, I. Meiri, and J. Pearl. Temporal constraint networks. Artificial Intelligence, 49:61-95, 1991.
114. H. A. Kautz and P. B. Ladkin. Integrating metric and qualitative temporal reasoning. In Proc. of the Ninth National Conference on Artificial Intelligence, pages 241-246, Anaheim, Calif., 1991.
115. I. Meiri. Combining qualitative and quantitative constraints in temporal reasoning. In Proc. of the Ninth National Conference on Artificial Intelligence, pages 260-267, Anaheim, Calif., 1991.
116. B. Nebel and H.-J. Burckert. Reasoning about temporal relations: A maximal tractable subclass of Allen's interval algebra. J. ACM, 42:43-66, 1995.
117. P. van Beek. Temporal query processing with indefinite information. Artificial Intelligence in Medicine, Special Issue on Temporal Reasoning, 3:325-339, 1991.
118. P. van Beek. Reasoning about qualitative temporal information. Artificial Intelligence, 58:297-326, 1992.
119. P. van Beek and R. Cohen. Exact and approximate reasoning about temporal relations. Computational Intelligence, 6:132-144, 1990.
120. M. Vilain and H. Kautz. Constraint propagation algorithms for temporal reasoning. In Proc. of the Fifth National Conference on Artificial Intelligence, pages 377-382, Philadelphia, Pa., 1986.

Applications

121. M. B. Clowes. On seeing things. Artificial Intelligence, 2:79-116, 1971.
122. J. M. Crawford and A. B. Baker. Experimental results on the application of satisfiability algorithms to scheduling problems. In Proceedings of the Twelfth National Conference on Artificial Intelligence, Seattle, Wash., 1994.
123. R. Feldman and M. C. Golumbic. Constraint satisfiability algorithms for interactive student scheduling. In IJCAI-89, pages 1010-1016, Detroit, Mich., 1989.
124. E. C. Freuder. On the knowledge required to label a picture graph. Artificial Intelligence, 15:1-17, 1980.
125. D. A. Huffman. Impossible objects as nonsense sentences. In B. Meltzer and D. Michie, editors, Machine Intelligence 6, pages 295-323. Edinburgh Univ. Press, 1971.
126. H. Maruyama. Structural disambiguation with constraint propagation. In Proc. of the 28th Conference of the Association for Computational Linguistics, pages 31-38, Pittsburgh, Pennsylvania, 1990.
127. S. Morito, H. M. Salkin, and D. E. Williams. Two backtrack algorithms for the radio frequency intermodulation problem. Appl. Math. Optim., 6:221-240, 1980.
128. M. Yoshikawa, K. Kaneko, Y. Nomura, and M. Watanabe. A constraint-based approach to high-school timetabling problems: A case study. In Proceedings of the Twelfth National Conference on Artificial Intelligence, pages 1111-1116, Seattle, Wash., 1994.
129. D. Waltz. Understanding line drawings of scenes with shadows. In P. H. Winston, editor, The Psychology of Computer Vision, pages 19-91. McGraw-Hill, 1975.
130. P. van Beek and X. Chen. CPlan: A Constraint Programming approach to Planning. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, pages 585-590, Orlando, Florida, 1999.

Miscellaneous

131. E. C. Freuder. Completable representations of constraint satisfaction problems. In Proc. of the Second International Conference on Principles of Knowledge Representation and Reasoning, pages 186-195, Cambridge, Mass., 1991.
132. E. C. Freuder and P. D. Hubbe. Extracting constraint satisfaction subproblems. In Proc. of the Fourteenth International Joint Conference on Artificial Intelligence, pages 548-557, Montreal, Quebec, 1995.
133. E. C. Freuder and R. J. Wallace. Generalizing inconsistency learning for constraint satisfaction. In Proc. of the Fourteenth International Joint Conference on Artificial Intelligence, pages 563-571, Montreal, Quebec, 1995.
134. M. Gyssens, P. G. Jeavons, and D. A. Cohen. Decomposing constraint satisfaction problems using database techniques. Artificial Intelligence, 66:57-90, 1994.
135. P. D. Hubbe and E. C. Freuder. An efficient cross-product representation of the constraint satisfaction problem search space. In Proceedings of the Tenth National Conference on Artificial Intelligence, pages 421-427, San Jose, Calif., 1992.
136. P. Jegou. Decomposition of domains based on the micro-structure of finite constraint satisfaction problems. In Proc. of the Eleventh National Conference on Artificial Intelligence, pages 731-736, Washington, DC, 1993.
137. S. Mittal and F. Frayman. Making partial choices in constraint reasoning problems. In Proc. of the Sixth National Conference on Artificial Intelligence, pages 631-636, Seattle, Wash., 1987.
138. U. Montanari and F. Rossi. Fundamental properties of networks of constraints: A new formulation. In L. Kanal and V. Kumar, editors, Search in Artificial Intelligence, pages 426-449. Springer-Verlag, 1988.
139. I. Rivin and R. Zabih. An algebraic approach to constraint satisfaction problems. In Proc. of the Eleventh International Joint Conference on Artificial Intelligence, pages 284-289, Detroit, Mich., 1989.
140. T. Schiex and G. Verfaillie. Nogood recording for static and dynamic constraint satisfaction problems. International Journal on Artificial Intelligence Tools, 3:1-15, 1994.
141. G. Verfaillie and T. Schiex. Solution reuse in dynamic constraint satisfaction problems. In Proceedings of the Twelfth National Conference on Artificial Intelligence, Seattle, Wash., 1994.
142. N. Yugami. Theoretical analysis of Davis-Putnam procedure and propositional satisfiability. In Proc. of the Fourteenth International Joint Conference on Artificial Intelligence, pages 282-288, Montreal, Quebec, 1995.