Prioritised fuzzy constraint satisfaction problems: Axioms, instantiation and validation

Fuzzy Sets and Systems

Volumn 136, Issue 2, 2003, Pages 151-188

  Luo, Xudong ^a   Lee, Jimmy Ho Man ^b   Leung, Ho Fung ^b   Jennings, Nicholas R ^a  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Constraint satisfaction; Decision making; Fuzzy set theory; Knowledge engineering; Non monotonicity; Priority; Weight

Indexed keywords

CONSTRAINT THEORY; DECISION MAKING; KNOWLEDGE ENGINEERING; PROBLEM SOLVING;

NON-MONOTONICITY;

FUZZY SETS;

EID: 0038220050     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-0114(02)00385-8     Document Type: Article

References (84)

1. Aczel J., Saaty T. Procedures for synthesizing ratio judgements. J. Math. Psychol. 27:1983;93-102.
2. Altman R.B., Brinkley J.F. Probabilistic constraint satisfaction with structural models. application to organ modeling by radial contours Proc. Ann. Symp. on Computer Applications in Medical Care. 1993;492-496.
3. Azuma K, Weighted sums of certain dependent random variables. Tohoku Math. J. 19:1967;357-367.
4. Barbuceanu M., Lo W.-K. A multi-attribute utility theoretic negotiation architecture for electronic commerce. Proc. 4th Internat. Conf. on Autonomous Agents. 2000;239-246.
5. Bellman R.E., Ciertz M. On the analytic formalism of the theory of fuzzy sets. Inform. Sci. 5:1973;149-156.
6. Bellman R.E., Zadeh L.A. Decision making in a fuzzy environment. Manage. Sci. P17-B(4):1970;141-164.
7. Bessire C. Arc-consistency and arc-consistency again. Proc. 11th Natl. Conf. on Artificial Intelligence. 1994.
8. Bistarelli S., Fargier H., Montanari U., Rossi F., Schiex T., Verfailie G. Semiring-based constraint and valued CSPs. frameworks, properties, and comparison Constraint: Internat. J. 4(3):1999;275-316.
9. Bistarelli S., Montanari U., Rossi F. Constraint solving over semirings. Proc. 14th Internat. Joint Conf. on Artificial Intelligence. 1995;624-630.
10. Bistarelli S., Montanari U., Rossi F. Semirings-based constraint logic programming. Proc. 15th Internat. Joint Conf. on Artificial Intelligence. 1997.
11. Bowen J., Dozier G. Solving randomly generated fuzzy constraint networks using interactive microevolutionary hill-climbing. Proc. 1st Internat. Symp. on Soft Computing in Industry. 1996.
12. Bryson N., Joseph A. Generating consensus priority interval vectors for group decision-making in the AHP. J. Multi-Criteria Decision Anal. 9:2000;127-137.
13. Carlsson C., Fullr R. Benchmarking in linguistic importance weighted aggregations. Fuzzy Sets and Systems. 114(1):2000;35-41.
14. Cost S., Salzberg S. A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning. 10:1993;57-78.
15. Coifman R.R., Fefferman R. Weighted norm inequalities for maximal functions and singular integrals. Studia Math. 51:1974;241-250.
16. Dago P., Verfaillie G. Nogood recordings for valued constraint satisfaction problems. Proc. 8th IEEE Internat. Conf. on Tools with Artificial Intelligence. 1996;132-139.
17. D. Dubois, H. Fargier, H. Prade, Flexible constraint satisfaction with application to scheduling problems, Report IRIT/93-30-R, Institute de Recherche en Informatique de Toulouse, 1993.
18. D. Dubois, H. Fargier, H. Prade, The calculus of fuzzy restrictions as a basis for flexible constraint satisfaction,in: Proc. IEEE Internat. Conf. on Fuzzy Systems, 1993.
19. Dubois D., Fargier H., Prade H. Propagation and satisfaction of flexible constraints. Yager R., Zadeh L. Fuzzy Sets, Neural Networks and Soft Computing. 1994;166-187.
20. Dubois D., Fargier H., Prade H. Possibility theory in constraint satisfaction problems. handling priority, preference and uncertainty Appl. Intelligent. 6(4):1996;287-309.
21. Dubois D., Prade H. A class of fuzzy measures based on triangular norms. Internat. J. General Systems. 8:1982;1.
22. Dubois D., Prade H. Possibility theory as a basis for qualitative decision theory. Proc. 14th Internat. Conf. on Artificial Intelligence. 1995;1924-1930.
23. Dubois D., Prade H. Qualitative possibility theory and its applications to constraint satisfaction and decision under uncertainty. Internat. J. Intelligent Systems. 14:1999;45-61.
24. Dubois D., Prade H., Sabbadin R. Decision under qualitative uncertainty with Sugeno integrals. an axiomatic approach Actes du 7eme Congrs Mondial IFSA'97. 1997;441-446.
25. Dubois D., Prade H., Sabbadin R. Qualitative decision theory with Sugeno integrals. Proc. 14th Conf. on Uncertainty in Artificial Intelligence. 1998;121-128.
26. Duda R.O., Hart P.E., Nilsson N.J., Reboh R., Slocum J., Sutherland G. Subjective Bayesian methods for rule-based inference systems. AFIPS Conf. Proc. Vol. 45:1976;1075-1082.
27. Fargier H., Lang J. Uncertainty in constraint satisfaction problems. a probabilistic approach Proc. European Conf. on Symbolic and Qualitative Approaches to Reasoning and Uncertainty. Lecture Notes in Computer Science. Vol. 747:1993;97-104 Springer, Berlin.
28. Feddema J.T., Lee C.S.G., Mitchell O.R. Weighted selection of image features for resolved rate visual feedback control. IEEE Trans. Robotics Automation. 7(1):1991;31-47.
29. Fichtner J. On deriving priority vectors from matrices of pairwise comparisons. Socio-Economic Planning Sci. 20(6):1986;399-405.
30. Fodor J. Structure of uninorms. Internat. J. Uncertainty, Fuzziness Knowledge-Based Systems. 5(4):1998;411-427.
31. Fox M.S., Sedeh N. Why is scheduling difficult? A CSP perspective. Proc. 9th European Conf. on Artificial Intelligence. 1990;754-767.
32. Fuller R., Carlsson C. Fuzzy multiple criteria decision making. recent developments Fuzzy Sets and Systems. 78:1996;139-153.
33. Gennari R. Soft constraint propagation: generalization and termination conditions. Proc. 6th Conf. of Principle and Practice of Constraint Programming. 2000.
34. Grabisch M. Fuzzy integral in multicriteria decision making. Fuzzy Sets and Systems. 69(3):1995;279-298.
35. Guttman R.H., Maes P. Agent-mediated integrative negotiation for retail electronic commerce. Noriega P., Sierra C. Agent Mediated Electronic Commerce. Lecture Notes in Artificial Intelligence. Vol. 1571:1998;70-90 Springer, Berlin.
36. Hong T.H., Rosenfeld A. Compact region extraction using weighted pixel linking in a pyramid. IEEE Trans. on Pattern Anal. Machine Intelligence. 6(2):1984;222-229.
37. Kam R.W.L., Lee J.H.-M. Fuzzifying the constraint hierarchies framework. Proc. 4th Internat. Conf. on Principles and Practices of Constraint Programming. Lecture Notes in Artificial Intelligence. Vol. 1520:1998;280-294 Springer, Berlin.
38. Klement E.P., Mesiar R., Pap E. On the relationship of associative compensatory operators to triangular norms and conorms. Internat. J. Uncertainty, Fuzziness Knowledge-Based Systems. 4(2):1996;129-144.
39. R. Kowalczyk, On negotiation as a distributed fuzzy constraint satisfaction problem, in: Proc. 3rd Internat. Symp. on Soft Computing for Industry of the World Automation Congress, 2000, pp. 631-637.
40. R. Kowalczyk, V. Bui, On fuzzy e-negotiation agents: autonomous negotiation with incomplete and imprecise information, in: Proc. DEXA e-Negation Workshop, 2000.
41. Kowalczyk R., Bui V. On constraint-based reasoning in e-negotiation agents. Dignum F., Cortés U. Agent Mediated E-commerce III. Lecture Notes in Artificial Intelligence. Vol. 2003:2000;31-46 Springer, Berlin.
42. M. Lemaître, G. Verfaillie, An incomplete method for solving distributed valued constraint satisfaction problems, in: Proc. AAAI97 Workshop on "Constraints and Agents" Providence, RI, USA, 1997.
43. Li Y.-M., Shi Z.-K. Remarks on uninorm aggregation operators. Fuzzy Sets and Systems. 114:2000;377-380.
44. X. Luo, N.R. Jennings, N.R. Shadbolt, H.-F. Leung, J.H.-H. Lee, A fuzzy constraint based model for bilateral, multi-issue negotiations in semi-competitive environments, Artificial Intelligence, submitted.
45. X. Luo, H.-F. Leung, J.H.-M. Lee, Theory and properties of a selfish protocol for multi-agent meeting scheduling using fuzzy constraints, in: Proc. 14th European Conf. on Artificial Intelligence, 2000, pp. 373-377.
46. Luo X., Zhang C. An axiom foundation for uncertain reasonings in rule-based expert systems. NT-algebra Knowledge Inform. Systems: Internat. J. 1(4):1999;415-433.
47. Luo X., Zhang C. Isomorphic transformations of uncertainties for incorporating EMYCIN-style and PROSPECTOR-style systems into a distributed expert system. J. Comput. Sci. Technol. 14(4):1999;368-392.
48. Luo X., Zhang C., Cai J. The weighting issue in fuzzy logic. Informat.: Internat. J. Comput. Inform. 21:1997;255-262.
49. Luo X., Zhang C., Leung H.-F. A class of isomorphic transformations for integrating EMYCIN-style and PROSPECTOR-style systems into a rule-based multi-agent system. C. Zhang H. Nakashima, Approaches to Intelligent Agents. Lecture Notes in Artificial Intelligence. Vol. 1733:1999;211-225 Springer, Berlin.
50. Luo X., Zhang C., Leung H.-F. Information sharing between heterogeneous uncertain reasoning models in a multi-agent environment. a case study Internat. J. Approximate Reasoning. 27(1):2001;27-59.
51. Mackworth A.K. Consistency in networks of relations. Artificial Intelligence. 8(1):1977;99-118.
52. Marichal J.-L. On Sugeno integral as an aggregation function. Fuzzy Sets and Systems. 114(3):2000;347-365.
53. McCarthy J. Circumscriptions. a form of non-monotonic reasoning Artificial Intelligence. 13:1980;27-39.
54. P. Meseguer, J. Larrosa, Solving fuzzy constraint satisfaction problems, in: Proc. 6th IEEE Internat. Conf. on Fuzzy Systems, Vol. 3, 1997, pp. 1233-1238.
55. Mohr R., Henderson T. Arc and path consistency revisited. Artificial Intelligence. 28:1986;225-233.
56. Motta E. Reusable Components for Knowledge Modelling: Case Studies in Parametric Design Problem Solving. 1999;IOS Press, Berlin.
57. C.V. Negoita, Management applications of system theory, Editura Tehnica, Bucharest and Birkhäuser Verlag, Basel, 1979.
58. Peasall J. The new Oxford dictionary of English. 2001;Oxford University Press, Oxford.
59. A.D. Pierro, H. Wiklicky, On probabilistic CCP, in: Proc. APPPIA-GULP-PRODE'97 Joint Conf. on Declarative Programming, June 1997.
60. A.D. Pierro, H. Wiklicky, An operational semantics for probabilistic concurrent constraint programming, in: Proc. Internat. Conf. on Computer Language, 1998, pp. 174-183.
61. Raughunathan S. A planning aid. an intelligent modelling system for planning problems based on constraint satisfaction IEEE Trans. on Knowledge Data Eng. 4(4):1992;317-335.
62. Reiter R. A logic for default reasoning. Artificial Intelligence. 13(1-2):1980;81-132.
63. Rosenfeld A., Hummel R., Zucker S. Scene labelling by relaxation operations. IEEE Trans. on Systems, Man Cybernet. 6(6):1976;420-433.
64. Z. Ruttkay, Fuzzy constraint satisfaction, in: Proc. IEEE Internat. Conf. on Fuzzy Systems, 1994.
65. Saaty T. The Analytic Hierarchy Process. 1980;McGraw-Hill, New York.
66. Sabbadin R., Dubois D., Prade H. A fuzzy constraint satisfaction problem in the wine industry. J. Intelligent Fuzzy Systems. 6:1998;361-374.
67. Sandholm T.W. Distributed rational decision making. Weiss G. Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. 1999;201-258 The MIT Press, Cambridge, MA.
68. T. Schiex, Valued constraint networks. in: Proc. 6th Conf. on Principles and Practice of Constraint Programming, 2000.
69. T. Schiex, H. Fargier, G. Verfaillie, Valued constraint satisfaction problems: hard and easy problems, in: Proc. Internat. Joint Conf. on Artificial Intelligence, 1995, pp. 631-637.
70. G. Schreiber, H. Akkermans, A. Anjewierden, R.d. Hoog, N. Shadbolt, W.V.d. Velde, B. Wielinga, Knowledge Engineering and Management: The CommonKADS Methodology, The MIT Press, Cambridge, MA, 1999, pp. 167-186 (Chapter 5).
71. Schweizer B., Sklar A. Associative functions and abstract semigroups. Publ. Math. Debrecen. 10:1963;69-81.
72. N.M. Shazeer, M.L. Littman, G.A. Keim, Solving crossword puzzles as probabilistic constraint satisfaction, in: Proc. 16th Natl. Conf. on Artificial Intelligence, 1999, pp. 156-162.
73. R. Sun, B. Chu, R. Wilhelm, J. Yao, A CSP-based model for integrated supply chain, in: Proc. AAAI-99 Workshop on AI for Electronic Commerce, 1999.
74. Tsang E.P.K. Foundations of Constraint Satisfaction. 1993;Academic Press, New York.
75. T. Tsuruta, T. Shintani, Scheduling meeting using distributed valued constraint satisfaction algorithm, in: Proc. 14th European Conf. on Artificial Intelligence, 2000, pp. 383-387.
76. J.H.Y. Wong, H.-F. Leung, Solving fuzzy constraint satisfaction problems with fuzzy GENET, in: Proc. 10th Internat. Conf. on Tools with Artificial Intelligence, 1998, pp. 184-191.
77. J.H.Y. Wong, H.-F. Leung, Extending GENET to solve fuzzy constraint satisfaction problems. Proc. AAAI-98, 1998, pp. 380-385.
78. R.R. Yager, Misrepresentations and challenges: a response to Elkan, IEEE Expert, August 1994, pp. 41-42.
79. Yager R.R., Rybalov A. Uninorm aggregation operators. Fuzzy Sets and Systems. 80:1996;111-120.
80. R.R. Yager, A. Rybalov, Uni-norms: a unification of T-norm and T-conorms, in: Proc. Nafips Conf., 1996, pp. 50-54.
81. Zadeh L.A. Fuzzy sets. Inform. Control. 8:1965;338-353.
82. C. Zhang, X. Luo, Isomorphic transformation of uncertainties of propositions among the EMYCIN and PROSPECTOR uncertain models, in: Proc. 2nd Internat. Conf. on Multi-Agent Systems, 1996, p. 465.
83. Zimmermann H.-J. Fuzzy programming and linear programming with several objective function. Fuzzy Sets and Systems. 1:1978;45-55.
84. Zimmermann H.-J. Fuzzy Set Theory and Its Applications. 3rd ed. 1996;Kluwer Academic Publishers, Dordrecht.