Preferences in AI: An overview

Artificial Intelligence

Volumn 175, Issue 7-8, 2011, Pages 1037-1052

  Domshlak, Carmel ^a   Hüllermeier, Eyke ^b   Kaci, Souhila ^c   Prade, Henri ^d  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

^b PHILIPPS UNIVERSITY MARBURG   (Germany)

^c UNIVERSITÉ D'ARTOIS   (France)

^d UNIVERSITÉ PAUL SABATIER   (France)

Author keywords

Aggregation functions; Graphical representation; Logical representation; Preference; Preference learning; Preference queries

Indexed keywords

ARTIFICIAL INTELLIGENCE;

AGGREGATION FUNCTIONS; GRAPHICAL REPRESENTATIONS; LOGICAL REPRESENTATIONS; PREFERENCE; PREFERENCE LEARNING; PREFERENCE QUERIES;

OPERATIONS RESEARCH;

EID: 79954586102     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artint.2011.03.004     Document Type: Editorial

References (278)

1. C. Boutilier, T. Dean, and S. Hanks Decision-theoretic planning: Structural assumptions and computational leverage J. Artif. Intell. Res. (JAIR) 11 1999 1 94 (Pubitemid 129628760)
2. L. Savage The Foundations of Statistics 1954 J. Wiley New York 2nd rev. ed. 1972 Dover New York
3. J. Doyle Prospects for preferences Comput. Intell. 20 2 2004 111 136
4. C. Boutilier, R.I. Brafman, H.H. Hoos, D. Poole, Reasoning with conditional ceteris paribus preference statements, in: K.B. Laskey, H. Prade (Eds.), Proc. of the 15th Conf. on Uncertainty in Artificial Intelligence (UAI 99), Stockholm, July 30-Aug. 1, 1999, pp. 71-80.
5. C. Boutilier, R.I. Brafman, C. Domshlak, H.H. Hoos, and D. Poole CP-nets: A tool for representing and reasoning with conditional ceteris paribus preference statements J. Artif. Intell. Res. (JAIR) 21 2004 135 191 (Pubitemid 41525900)
6. G. Bosi, R. Brafman, J. Chomicki, and W. Kiessling (Orgs.) Preferences: Specification, inference, applications Dagstuhl, June 27-July 2, 2004 http://www.dagstuhl.de/04271/
7. R. Brafman, U. Junker (Orgs.), Multidisciplinary Workshop on Advances in Preference Handling, in conjunction with IJCAI-05, Edinburgh, July 31-Aug. 1, 2005.
8. U. Junker, W. Kiessling (Orgs.), 2nd Multidisciplinary Workshop on Advances in Preference Handling, at ECAI-06, Riva del Garda, Aug. 28-29, 2006.
9. J. Delgrande, W. Kiessling (Orgs.), 3rd Multidisciplinary Workshop on Advances in Preference Handling (M-PREF 2007), in conjunction with VLDB 2007, Vienna, Sept. 23, 2007.
10. J. Chomicki, V. Conitzer, U. Junker, P. Perny (Orgs.), 4th Multidisciplinary Workshop on Advances in Preference Handling (AAAI-08 Workshop M-PREF-08), Chicago, July 13-14, 2008.
11. U. Junker, J. Lang, P. Perny, in: 5th Multidisciplinary Workshop on Advances in Preference Handling (M-PREF), at ECAI-10, Lisbon, Aug. 16, 2010.
12. S. Bistarelli, F. Rossi, Special Issue: Preferences and Soft Constraints J. Heuristics 12 4-5 2006 239 392
13. J. Delgrande, J. Doyle, U. Junker, F. Rossi, T. Schaub, Special issue on Preferences in Artificial Intelligence (AI) and Constraint Programming (CP) Comput. Intell. 2004 109 443
14. J. Goldsmith, U. Junker, Special issue on Preference Handling for Artificial Intelligence AI Magazine 2008 9 103
15. A. Tversky, and D. Kahneman The framing of decisions and the psychology of choice Science 211 4481 1981 453 458 (Pubitemid 11145313)
16. S. Lichtenstein, and P. Slovic Preference reversals: A broader perspective Am. Econ. Rev. 73 1983 596 605
17. E. Raufaste, and D.J. Hilton A cognitive approach to human decision making D. Bouyssou, D. Dubois, M. Pirlot, H. Prade, Decision-Making Process: Concepts and Methods 2009 Wiley 475 503 (Ch. 12)
18. J. von Neumann, and O. Morgenstern Theory of Games and Economic Behaviour 2nd ed. 1947 Princeton University Press Princeton
19. K.J. Arrow Social Choice and Individual Values 2nd ed. 1963 Wiley New York
20. M. Tennenholtz Game theory and Artificial Intelligence M. dInverno, M. Luck, M. Fisher, C. Preist, Foundations and Applications of Multi-Agent Systems, UKMAS Workshop 1996-2000, Selected Papers Lecture Notes in Computer Science vol. 2403 2002 Springer 49 58
21. P.C. Fishburn Utility Theory for Decision Making 1970 John Wiley and Sons New York
22. D.H. Krantz, R. Luce, P. Suppes, and A. Tversky Foundations of Measurement, vol. 1: Additive and Polynomial Representations 1971 Academic Press Inc. New York
23. P.P. Wakker Additive Representations of Preferences: A New Formulation of Decision Analysis 1989 Kluwer Academic Publ. Dordrecht
24. D. Bouyssou, and M. Pirlot Conjoint measurement models for preference relations D. Bouyssou, D. Dubois, M. Pirlot, H. Prade, Decision-Making Process: Concepts and Methods 2009 Wiley 617 672 (Ch. 16)
25. A. Tversky Intransitivity of preferences Psychol. Rev. 76 1969 31 48
26. D. Bouyssou, and M. Pirlot Conjoint measurement without additivity and transitivity N. Meskens, M. Roubens, Advances in Decision Analysis 1999 Kluwer Academic Publ. Dordrecht 13 29
27. D. Bouyssou, and M. Pirlot Following the traces: An introduction to conjoint measurement without transitivity and additivity European J. Oper. Res. 163 2005 287 337
28. S.S. Stevens On the theory of scales of measurement Science 103 2684 1946 677 680
29. F.S. Roberts Measurement Theory: with Applications to Decision Making, Utility, and the Social Sciences Encyclopedia of Mathematics and its Applications vol. 7 1979 Addison-Wesley Reading, MA
30. R.R. Yager On ordered weighted averaging aggregation operators in multi-criteria decision making IEEE Trans. Syst. Man Cybernet. 18 1988 183 190
31. R.R. Yager, and J. Kacprzyk The Ordered Weighted Averaging Operators: Theory and Applications 1997 Kluwer Academic Publ. Norwell, MA
32. D. Dubois, H. Fargier, and H. Prade Beyond min aggregation in multicriteria decision: (Ordered) weighted min, discri-min, leximin R. Yager, J. Kacprzyk, The Ordered Weighted Averaging Operators - Theory and Applications 1997 Kluwer Academic Publ. 181 192
33. J. Fodor, and M. Roubens Fuzzy Preference Modelling and Multicriteria Decision Support 1994 Kluwer Academic Publ. Dordrecht
34. M. Grabisch, S.A. Orlovski, and R.R. Yager Fuzzy aggregation of numerical preferences R. Słowiński, Fuzzy Sets in Decision Analysis, Operations Research and Statistics 1998 Kluwer Academic Publ. Norwell, MA
35. T. Calvo, G. Mayor, and R. Mesiar Aggregation Operators: New Trends and Applications 2002 Physica Verlag Heidelberg
36. J.-L. Marichal Aggregation functions for decision making D. Bouyssou, D. Dubois, M. Pirlot, H. Prade, Decision-Making Process: Concepts and Methods 2009 Wiley 673 721 (Ch. 17)
37. M. Grabisch, J.-L. Marichal, R. Mesiar, and E. Pap Aggregation functions: Means Inform. Sci. 181 2011 1 22
38. M. Grabisch, J.-L. Marichal, R. Mesiar, and E. Pap Aggregation functions: Construction methods, conjunctive, disjunctive and mixed classes Inform. Sci. 181 2011 23 43
39. P.C. Fishburn Preferences structures and their numerical representation Theoret. Comput. Sci. 217 1999 359 383
40. M. Grabisch, and C. Labreuche A decade of application of the Choquet and Sugeno integrals in multi-criteria decision aid Quart. J. Oper. Res. 6 2008 1 44 updated version: Ann. Oper. Res. 175 2010 247 286
41. M. Grabisch, and C. Labreuche The symmetric and asymmetric Choquet integrals on finite spaces for decision making Statist. Papers 43 2002 37 52 (Pubitemid 41388674)
42. C. Labreuche, and M. Grabisch The Choquet integral for the aggregation of interval scales in multicriteria decision making Fuzzy Sets Syst. 137 2003 11 26
43. M. Grabisch, and M. Roubens Application of the Choquet integral in multicriteria decision making M. Grabisch, T. Murofushi, M. Sugeno, Fuzzy Measures and Integrals Theory and Applications 2000 Physica Verlag 415 434
44. M. Grabisch, T. Murofushi, and M. Sugeno Fuzzy Measures and Integrals: Theory and Applications 2000 Physica Verlag Heidelberg
45. M. Sugeno Fuzzy measures and fuzzy integrals: A survey M. Gupta, G. Saridis, B.R. Gaines, Fuzzy Automata and Decision Processes 1977 North-Holland Amsterdam 89 102
46. D. Dubois, J.-L. Marichal, H. Prade, M. Roubens, and R. Sabbadin The use of the discrete Sugeno integral in decision-making: A survey Internat. J. Uncertain. Fuzziness Knowledge-Based Systems 9 2001 539 561 (Pubitemid 33681875)
47. M. Grabisch The symmetric Sugeno integral Fuzzy Sets Syst. 139 2003 473 490
48. A. Rico, M. Grabisch, C. Labreuche, and A. Chateauneuf Preference modeling on totally ordered sets by the Sugeno integral Discrete Appl. Math. 147 2005 113 124 (Pubitemid 40206496)
49. M. Grabisch, C. Labreuche, The Šipoš integral for the aggregation of interacting bipolar criteria, in: Proc. 8th Inter. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Madrid, 2000, pp. 395-401.
50. M. Grabisch, and C. Labreuche Bi-capacities - I. Definition, Möbius transform and interaction Fuzzy Sets Syst. 151 2005 211 236 (Pubitemid 40231849)
51. M. Grabisch, and C. Labreuche Bi-capacities - II: The Choquet integral Fuzzy Sets Syst. 151 2005 237 259 (Pubitemid 40226404)
52. M. Grabisch Aggregation on bipolar scales H. de Swart, E. Orlowska, M. Roubens, G. Schmidt, Theory and Applications of Relational Structures as Knowledge Instruments II Lecture Notes in Computer Science vol. 4342 2006 Springer 355 371
53. M. Grabisch, S. Greco, and M. Pirlot Bipolar and bivariate models in multicriteria decision analysis: Descriptive and constructive approaches Int. J. Intell. Syst. 23 2008 930 969
54. A. Tversky, and D. Kahneman Advances in prospect theory: Cumulative representation of uncertainty J. Risk Uncertainty 5 1992 297 323
55. H. Fargier, J. Lang, T. Schiex, Selecting preferred solutions in fuzzy constraint satisfaction problems, in: Proc. of the 1st Europ. Cong. on Fuzzy and Intelligent Technologies (EUFIT93), Aachen, 1993, pp. 1128-1134.
56. H. Moulin Axioms of Cooperative Decision Making 1988 Cambridge University Press Cambridge, MA
57. A. Marshall, and I. Olkin Inequalities: A Theory of Majorization and its Applications 1979 Academic Press New York
58. P. Perny, and O. Spanjaard An axiomatic approach to robustness in search problems with multiple scenarios C. Meek, U. Kjærulff, Proc. 19th Conf. in Uncertainty in Artificial Intelligence (UAI 03) Acapulco, Aug. 7-10 2003 Morgan Kaufmann 469 476
59. B. Golden, P. Perny, Infinite order Lorenz dominance for fair multiagent optimization, in: Proc. Inter. Conf. on Autonomous Agents and Multiagent Systems, 2010, pp. 383-390.
60. D.E. Campbell, and J.S. Kelly Impossibility theorems in the Arrovian framework Handbook of Social Choice and Welfare, vol. 1 2002 Elsevier 35 94
61. M. Roubens, and P. Vincke Preference Modelling 1985 Springer Berlin
62. L. Kitainik Fuzzy Decision Procedures with Binary Relations: Towards a Unified Theory 1993 Kluwer Academic Publ. Dordrecht
63. S.W. Tan, J. Pearl, Qualitative decision theory, in: Proc. 12th Nat. Conf. on Artificial Intelligence (AAAI-94), Seattle, 1994, pp. 928-933.
64. C. Boutilier, Toward a logic for qualitative decision theory, in: J. Doyle, E. Sandewall, P. Torasso (Eds.), Proc. 4th International Conf. on Principles of Knowledge Representation and Reasoning (KR94), Bonn, May 24-27, 1994, pp. 75-86.
65. D. Dubois, H. Prade, Possibility theory as a basis for qualitative decision theory, in: 14th Inter. Joint Conf. on Artificial Intelligence (IJCAI95), Montréal, 1995, pp. 1924-1930.
66. F. Bacchus, A.J. Grove, Utility independence in a qualitative decision theory, in: Proc. 5th Inter. Conf. on Principles of Knowledge Representation and Reasoning (KR96), Cambridge, MA, 1996, pp. 542-552.
67. J. Doyle, and R.H. Thomason Background to qualitative decision theory AI Magazine 20 2 1999 55 68
68. D. Dubois, H. Prade, and R. Sabbadin Decision-theoretic foundations of qualitative possibility theory European J. Oper. Res. 128 2001 459 478 (Pubitemid 32034396)
69. H.D. Dubois, H. Fargier, and R. Sabbadin A survey of qualitative decision rules under uncertainty D. Bouyssou, D. Dubois, M. Pirlot, H. Prade, Decision-Making Process: Concepts and Methods 2009 Wiley 435 473 (Ch. 11)
70. P.H. Giang, and P.P. Shenoy Two axiomatic approaches to decision making using possibility theory European J. Oper. Res. 162 2005 450 467
71. D. Dubois, H. Fargier, H. Prade, and P. Perny Qualitative decision theory: From Savages axioms to nonmonotonic reasoning J. ACM 49 4 2002 455 495
72. D. Dubois, H. Fargier, and P. Perny Qualitative decision theory with preference relations and comparative uncertainty: An axiomatic approach Artificial Intelligence 148 1-2 2003 219 260
73. D. Dubois, H. Fargier, and P. Perny Corrigendum to "Qualitative decision theory with preference relations and comparative uncertainty: an axiomatic approach" [Artificial Intelligence 148 (1-2) (2003) 219-260] Artificial Intelligence 171 2007 361 362 (Pubitemid 46578203)
74. D. Dubois, H. Fargier, Qualitative decision making with bipolar information, in: Proc. 10th Inter. Conf. on Principles of Knowledge Representation and Reasoning, Lake District, UK, June 2-5, 2006, pp. 175-186.
75. D. Dubois, H. Fargier, and J.-F. Bonnefon On the qualitative comparison of decisions having positive and negative features J. Artif. Intell. Res. (JAIR) 32 2008 385 417
76. D. Bouyssou, and P. Vincke Binary relations and preference modeling D. Bouyssou, D. Dubois, M. Pirlot, H. Prade, Decision-Making Process: Concepts and Methods 2009 Wiley 49 84 (Ch. 2)
77. R.I. Brafman, and C. Domshlak Graphically structured value-function compilation Artificial Intelligence 172 2-3 2008 325 349 (Pubitemid 350256735)
78. N. Wilson Efficient inference for expressive comparative preference languages C. Boutilier, Proc. 21st Inter. Joint Conf. on Artificial Intelligence (IJCAI 2009) July 11-17, 2009 Pasadena 961 966
79. R.I. Brafman, and C. Domshlak Preference handling - An introductory tutorial AI Magazine 30 1 2009 58 86
80. A.S. Rao, M.P. Georgeff, BDI agents: From theory to practice, in: Proc. 1st Inter. Conf. on Multi-Agent Systems (ICMAS-95), 1997, pp. 312-319.
81. F. Dignum, D. Kinny, and L. Sonenberg From desires, obligations and norms to goals Cognitive Sci. Quart. 2 2002 407 430
82. G. Brewka Preferred subtheories: An extended logical framework for default reasoning N.S. Sridharan, Proc. 11th Inter. Joint Conf. on Artificial Intelligence, Detroit 1989 Morgan Kaufmann 1043 1048
83. J. Doyle, and M.P. Wellman Impediments to universal preference-based default theories Artificial Intelligence 49 1991 97 128 (Pubitemid 21679935)
84. S. Kraus, D.J. Lehmann, and M. Magidor Nonmonotonic reasoning, preferential models and cumulative logics Artificial Intelligence 44 1990 167 207 (Pubitemid 20719858)
85. D.J. Lehmann, and M. Magidor What does a conditional knowledge base entail? Artificial Intelligence 55 1992 1 60
86. S. Benferhat, D. Dubois, and H. Prade Nonmonotonic reasoning, conditional objects and possibility theory Artificial Intelligence 92 1997 259 276 (Pubitemid 127414146)
87. M. Gelfond, and T.C. Son Reasoning with prioritized defaults J. Dix, L.M. Pereira, T.C. Przymusinski, Selected Papers of the 3rd Inter. Workshop on Logic Programming and Knowledge Representation (LPKR 97) Port Jefferson, NY, Oct. 17, 1997 Lecture Notes in Computer Science vol. 1471 1997 Springer 164 223 (Pubitemid 128134781)
88. J.P. Delgrande, T. Schaub, and H. Tompits A framework for compiling preferences in logic programs Theory and Practice of Logic Programming (TPLP) 3 2 2003 129 187
89. T. Schaub, and K. Wang A semantic framework for preference handling in answer set programming Theory and Practice of Logic Programming (TPLP) 3 4-5 2003 569 607
90. M. Freund On the revision of preferences and rational inference processes Artificial Intelligence 152 1 2004 105 137
91. S. Modgil Reasoning about preferences in argumentation frameworks Artificial Intelligence 173 2009 901 934
92. L. Amgoud, and S. Vesic Repairing preference-based argumentation systems C. Boutilier, Proc. Inter. Joint Conf. on Artificial Intelligence (IJCAI) Pasadena, July 13-17 2009 AAAI Press 365 370
93. L. Amgoud, S. Vesic, On the role of preferences in argumentation frameworks, in: Proc. 22nd IEEE Inter. Conf. on Tools with Artificial Intelligence (ICTAI), Arras, France, Oct. 27-29, 2010, pp. 219-222.
94. S. Kaci Refined preference-based argumentation frameworks 3rd International Conference on Computational Models of Argument (COMMA10) 2010 IOS Press 299 310
95. S. Kaci, and C. Labreuche Argumentation framework with fuzzy preference relations Proc. 13th Inter. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU10) Lecture Notes in Computer Science vol. 6178 2010 Springer 554 563
96. M. Lacroix, P. Lavency, Preferences: Putting more knowledge into queries, in: Proc. of the 13th Inter. Conference on Very Large Databases (VLDB87), 1987, pp. 217-225.
97. A. Motro A user interface to relational databases that permits vague queries ACM Trans. Inform. Syst. 6 1988 187 214
98. J. Chomicki Preference formulas in relational queries ACM Trans. Database Syst. 28 2003 1 40
99. W. Kießling, Foundations of preferences in database systems, in: Proc. of the 28th International Conference on Very Large Data Bases (VLDB02), 2002, pp. 311-322.
100. W. Kießling, G. Köstler, Preference SQL - Design, implementation, experiences, in: Proc. of the 28th Conference on Very Large Data Bases (VLDB02), 2002, pp. 990-1001.
101. J. Chomicki Database querying under changing preferences Ann. Math. Artif. Intell. 50 2007 79 109
102. P. Georgiadis, I. Kapantaidakis, V. Christophides, E. Nguer, N. Spyratos, Efficient rewriting algorithms for preference queries, in: Proc. of ICDE 2008, 2008, pp. 1101-1110.
103. R. Agrawal, R. Rantzau, E. Terzi, Context-sensitive ranking, in: S. Chaudhuri, V. Hristidis, N. Polyzotis (Eds.), Proc. 25th ACM SIGMOD Inter. Conf. on Management of Data, Chicago, June 27-29, 2006, pp. 383-394. (Pubitemid 46946526)
104. R.I. Brafman, C. Domshlak, Database preference queries revisited, Technical Report TR2004-1934, Cornell University, Computing and Information Science, 2004.
105. P. Ciaccia, Querying databases with incomplete CP-nets, in: Multidisciplinary Workshop on Advances in Preference Handling (M-PREF 2007), 2007.
106. A. HadjAli, S. Kaci, and H. Prade Database preferences queries: a possibilistic logic approach with symbolic priorities S. Hartmann, G. Kern-Isberner, Proc. 5th Inter. Symp. on Foundations of Information and Knowledge Systems (FoIKS08) Pisa, February 11-15 Lecture Notes in Computer Science vol. 4932 2008 Springer 291 310
107. R. Da Silva Neves, and S. Kaci Combining totalitarian and ceteris paribus semantics in database preference queries Log. J. IGPL 18 3 2010 464 483
108. P. Bosc, and O. Pivert SQLf: A relational database language for fuzzy querying IEEE Trans. Fuzzy Syst. 3 1995 1 17
109. F.E. Petry Fuzzy Databases: Principles and Applications 1997 Kluwer Academic Publ. Norwell, MA
110. G. Pasi Modeling users preferences in systems for information access Int. J. Intell. Syst. 18 2003 793 808
111. D. Dubois, and H. Prade Handling bipolar queries in fuzzy information processing Handbook of Research on Fuzzy Information Processing in Databases 2008 IGI Global Publication 97 114
112. P. Bosc, O. Pivert, On a parameterized antidivision operator for database flexible querying, in: Proc. of the 19th Conference on Database and Expert Systems Applications (DEXA08), 2008, pp. 652-659.
113. G. Bordogna, and G. Pasi A flexible model for the evaluation of soft conditional preferences in fuzzy databases Int. J. Intell. Syst. 23 2008 1264 1281
114. S. Zadrozny, and J. Kacprzyk Issues in the practical use of the OWA operators in fuzzy querying J. Intell. Inform. Syst. 33 2009 307 325
115. G. Bordogna, and G. Pasi A fuzzy linguistic approach generalizing Boolean information retrieval: A model and its evaluation JASIS 44 1993 70 82
116. R. Fagin, Fuzzy queries in multimedia database systems, in: Proc. of PODS 1998, 1998, pp. 1-10.
117. R. Agrawal, E. Wimmers, A framework for expressing and combining preferences, in: Proc. of SIGMOD00, 2000, pp. 297-306.
118. S. Börzsönyi, D. Kossmann, K. Stocker, The skyline operator, in: Proc. 17th IEEE International Conference on Data Engineering, 2001, pp. 421-430. (Pubitemid 32457144)
119. P. Godfrey, W. Ning, Relational preference queries via stable skyline, Technical Report CS-2004-03, York University, 2004, pp. 1-14.
120. C. Chan, H. Jagadish, K. Tan, A. Tung, Z. Zhang, Finding k-dominant skylines in high dimensional space, in: ACM SIGMOD06, 2006, pp. 503-514. (Pubitemid 46946535)
121. J.H. Chan, C.K. Tan, A. Tung, and Z. Zhang On high dimensional skylines Proc. of EDBT06 Lecture Notes in Computer Science vol. 3896 2006 Springer 478 495 (Pubitemid 43986837)
122. X. Lin, Y. Yuan, Q. Zhang, Y. Zhang, Selecting stars: The k most representative skyline operator, in: Proc. ICDE07, 2007, pp. 86-95. (Pubitemid 47422012)
123. J. Pei, B. Jiang, X. Lin, Y. Yuan, Probabilistic skylines on uncertain data, in: Proc. VLDB07, 2007, pp. 15-26.
124. M. Khalefa, M. Mokbel, J. Levandoski, Skyline query processing for incomplete data, in: Proc. ICDE08, 2008, pp. 556-565.
125. J. Lee, G. You, and S. Hwang Optimal preferences elicitation for Skyline queries over categorical domains Proc. of DEXA08 Conf. Lecture Notes in Computer Science vol. 5181 2008 Springer 610 624
126. M. Goncalves, L. Tineo, Fuzzy dominance skyline queries, in: Proc. of DEXA07, 2007, pp. 469-478.
127. E. Hüllermeier, I. Vladimirskiy, B. Prados Suárez, E. Stauch, Supporting case-based retrieval by similarity skylines: Basic concepts and extensions, in: Proc. of ECCBR08, 2008, pp. 240-254.
128. D. Sacharidis, A. Arvanitis, T. Sellis, Probabilistic contextual skylines, in: Proc. ICDE10, 2010, pp. 273-284.
129. A. HadjAli, O. Pivert, H. Prade, Possibilistic contextual skylines with incomplete preferences, in: Proc. 2nd IEEE Inter. Conf. on Soft Computing and Pattern Recognition (SoCPaR10), Cergy Pontoise, France, Dec. 7-10, 2010.
130. C. Domshlak A snapshot on reasoning with qualitative preference statements in AI G. Della Riccia, D. Dubois, R. Kruse, H.-J. Lenz, Preferences and Similarities CISM Courses and Lectures Series vol. 504 2008 Springer
131. R.I. Brafman, and Y. Dimopoulos Extended semantics and optimization algorithms for CP-networks Comput. Intell. 20 2 2004 218 245
132. C. Boutilier, F. Bacchus, and R.I. Brafman UCP-networks: A directed graphical representation of conditional utilities J.S. Breese, D. Koller, Proc. 17th Conf. in Uncertainty in Artificial Intelligence Seattle, August 2-5 2001 Morgan Kaufmann 56 64
133. R.I. Brafman, and C. Domshlak Introducing variable importance tradeoffs into CP-nets A. Darwiche, N. Friedman, Proc. 18th Conf. in Uncertainty in Artificial Intelligence (UAI 02) Edmonton, Aug. 1-4 2002 Morgan Kaufmann 69 76
134. C. Domshlak, S.D. Prestwich, F. Rossi, K.B. Venable, and T. Walsh Hard and soft constraints for reasoning about qualitative conditional preferences J. Heuristics 12 2006 263 285 (Pubitemid 43724146)
135. F. Rossi, K.B. Venable, and T. Walsh mCP nets: Representing and reasoning with preferences of multiple agents D.L. McGuinness, G. Ferguson, Proc. 19th Nat. Conf. on Artificial Intelligence (AAAI) San Jose, CA, July 25-29 2004 AAAI Press/The MIT Press 729 734
136. N. Wilson, Consistency and constrained optimisation for conditional preferences, in: R.L. de Mántaras, L. Saitta (Eds.), Proc. 16th Europ. Conf. on Artificial Intelligence (ECAI04), Valencia, Aug. 22-27, 2004, pp. 888-894.
137. N. Wilson Extending CP-nets with stronger conditional preference statements D.L. McGuinness, G. Ferguson, Proc. 19th Nat. Conf. on Artificial Intelligence (AAAI) San Jose, CA, July 25-29 2004 AAAI Press/The MIT Press 735 741
138. C. Domshlak, and R.I. Brafman CP-nets: Reasoning and consistency testing D. Fensel, F. Giunchiglia, D.L. McGuinness, M.-A. Williams, Proc. of the 8th Inter. Conf. on Principles and Knowledge Representation and Reasoning (KR-02) Toulouse, April 22-25 2002 Morgan Kaufmann 121 132
139. J. Goldsmith, J. Lang, M. Truszczynski, and N. Wilson The computational complexity of dominance and consistency in CP-nets J. Artif. Intell. Res. (JAIR) 33 2008 403 432
140. C. Domshlak, F. Rossi, K.B. Venable, and T. Walsh Reasoning about soft constraints and conditional preferences: complexity results and approximation techniques G. Gottlob, T. Walsh, Proc. of the 18th Inter. Joint Conf. on Artificial Intelligence (IJCAI-03) Acapulco, Aug. 9-15 2003 Morgan Kaufmann 215 220
141. N. Wilson An efficient upper approximation for conditional preference G. Brewka, S. Coradeschi, A. Perini, P. Traverso, Proc. 17th Europ. Conf. on Artificial Intelligence (ECAI 2006) Riva del Garda, Aug. 29-Sept. 1 2006 IOS Press 472 476
142. S. Kaci, and H. Prade Relaxing ceteris paribus preferences with partially ordered priorities K. Mellouli, Proc. 9th Europ. Conf. on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2007) Hammamet, Oct. 31-Nov. 2 Lecture Notes in Computer Science vol. 4724 2007 Springer 660 671
143. K.R. Apt, F. Rossi, K.B. Venable, CP-nets and Nash equilibria, CoRR abs/cs/0509071, 2005.
144. E. Bonzon, M.-C. Lagasquie-Schiex, J. Lang, and B. Zanuttini Compact preference representation and Boolean games Autonomous Agents and Multi-Agent Systems 18 2009 1 35
145. S. Bouveret, U. Endriss, J. Lang, Conditional importance networks: A graphical language for representing ordinal, monotonic preferences over sets of goods, in: C. Boutilier (Ed.), Proc. 21st Inter. Joint Conf. on Artificial Intelligence (IJCAI 2009), Pasadena, July 11-17, 2009, pp. 67-72.
146. F. Bacchus, and A. Grove Graphical models for preference and utility Proc. 11th Conf. on Uncertainty in Artificial Intelligence (UAI-95) 1995 Morgan Kaufmann 3 10
147. C. Gonzales, and P. Perny GAI networks for utility elicitation D. Dubois, C.A. Welty, M.-A. Williams, Proc. of the 9th Inter. Conf. on Principles of Knowledge Representation and Reasoning (KR2004) Whistler, June 2-5 2004 AAAI Press 224 234
148. C. Gonzales, P. Perny, S. Queiroz, Preference aggregation with graphical utility models, in: D. Fox, C.P. Gomes (Eds.), Proc. of the 23rd AAAI Conf. on Artificial Intelligence (AAAI 2008), Chicago, July 13-17, 2008, pp. 1037-1042.
149. J.-P. Dubus, C. Gonzales, P. Perny, Multiobjective optimization using GAI models, in: C. Boutilier (Ed.), Proc. 21st Inter. Joint Conf. on Artificial Intelligence (IJCAI 2009), Pasadena, July 11-17, 2009, pp. 1902-1907.
150. P. La Mura, Y. Shoham, Expected utility networks, in: K.B. Laskey, H. Prade (Eds.), Proc. of the 15th Conf. on Uncertainty in Artificial Intelligence (UAI99), Stockholm, July 30-Aug. 1, 1999, pp. 366-373.
151. Y. Engel, and M.P. Wellman CUI networks: A graphical representation for conditional utility independence J. Artif. Intell. Res. (JAIR) 31 2008 83 112 (Pubitemid 351761983)
152. J. Lang Logical representation of preferences D. Bouyssou, D. Dubois, M. Pirlot, H. Prade, Decision-Making Process: Concepts and Methods 2009 Wiley 321 363 (Ch. 7)
153. S. Coste-Marquis, J. Lang, P. Liberatore, P. Marquis, Expressive power and succinctness of propositional languages for preference representation, in: D. Dubois, C.A. Welty, M.-A. Williams (Eds.), Proc. 9th Inter. Conf. on Principles of Knowledge Representation and Reasoning (KR2004), Whistler, June 2-5, 2004, pp. 203-212.
154. Y. Chevaleyre, U. Endriss, J. Lang, Expressive power of weighted propositional formulas for cardinal preference modeling, in: P. Doherty, J. Mylopoulos, C.A. Welty (Eds.), Proc. 10th Inter. Conf. on Principles of Knowledge Representation and Reasoning, Lake District, UK, June 2-5, 2006, pp. 145-152.
155. F. Dupin de Saint-Cyr, J. Lang, T. Schiex, Penalty logic and its link with Dempster-Shafer theory, in: R.L. de Mántaras, D. Poole (Eds.), Proc. 10th Conf. on Uncertainty in Artificial Intelligence (UAI 94), Seattle, July 29-31, 1994, pp. 204-211.
156. C. Lafage, J. Lang, Logical representation of preferences for group decision making, in: Proc. 7th Inter. Conf. on Principles of Knowledge Representation and Reasoning (KR00), Breckenridge, Apr. 12-15, 2000, pp. 457-468.
157. D. Dubois, J. Lang, and H. Prade Possibilistic logic D.D. Gabbay, C. Hogger, J. Robinson, D. Nute, Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 3 1994 Oxford University Press 439 513
158. S. Benferhat, C. Cayrol, D. Dubois, J. Lang, and H. Prade Inconsistency management and prioritized syntax-based entailment R. Bajcsy, Proc. 13th Inter. Joint Conf. on Artificial Intelligence Chambry, Aug. 28-Sept. 3 1993 Morgan Kaufmann 640 647
159. C. Lafage, J. Lang, Logical representation of preferences for group decision making, in: Proc. of the 7th International Conference on Principles of Knowledge Representation and Reasoning (KR00), 2000, pp. 457-468.
160. S. Kaci, and L. van der Torre Reasoning with various kinds of preferences: logic, non-monotonicity, and algorithms Ann. Oper. Res. (AOR) 163 1 2008 89 114
161. J. Pearl System Z: A natural ordering of defaults with tractable applications to nonmonotonic reasoning R. Parikh, Proc. 3rd Conf. on Theoretical Aspects of Reasoning about Knowledge, Pacific Grove 1990 Morgan Kaufmann 121 135
162. M.P. Wellman, J. Doyle, Preferential semantics for goals, in: Proc. 9th Nat. Conf. on Artificial Intelligence (AAAI-91), Anaheim, July 14-19, 1991, pp. 698-703.
163. J. Doyle, Y. Shoham, and M.P. Wellman A logic of relative desire (Preliminary report) Z.W. Ras, M. Zemankova, Proc. 6th Inter. Symp. on Methodologies for Intelligent Systems (ISMIS 91) Charlotte, NC, Oct. 16-19 Lecture Notes in Computer Science vol. 542 1991 Springer 16 31
164. S. Benferhat, D. Dubois, and H. Prade Towards a possibilistic logic handling of preferences Appl. Intell. 14 3 2001 303 317 (Pubitemid 32542992)
165. S. Halldén On the Logic of "Better", Library of Theoria, vol. 2 1957 111 pp
166. G.H. von Wright The Logic of Preference 1963 Edinburgh University Press
167. S.O. Hansson A new semantical approach to the logic of preference Erkenntnis 31 1989 1 42
168. J. van Benthem, P. Girard, and O. Roy Everything else being equal: A modal logic approach to ceteris paribus preferences J. Philos. Logic 38 1 2009 83 125
169. M. Bienvenu, J. Lang, and N. Wilson From preference logics to preference languages, and back F. Lin, U. Sattler, M. Truszczynski, Proc. 12th Inter. Conf. on Principles of Knowledge Representation and Reasoning (KR 2010) Toronto, May 9-13 2010 AAAI Press
170. G. Brewka, S. Benferhat, and D.L. Berre Qualitative choice logic D. Fensel, F. Giunchiglia, D.L. McGuinness, M.-A. Williams, Proc. 8th Inter. Conf. on Principles and Knowledge Representation and Reasoning (KR-02) Toulouse, April 22-25 2002 Morgan Kaufmann 158 169
171. S. Benferhat, G. Brewka, D.L. Berre, On the relation between qualitative choice logic and possibilistic logic, in: Proc. 10th Inter. Conf. on Information Processing and Management of Uncertainty (IPMU04), Perugia, July 4-9, 2004, pp. 951-957.
172. P. Bosc, O. Pivert, H. Prade, A possibilistic logic view of preference queries to an uncertain database, in: Proc. 19th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE10), Barcelona, July 18-23, 2010, pp. 379-384.
173. G. Brewka, Logic programming with ordered disjunction, in: Proc. 18th Nat. Conf. on Artificial Intelligence (AAAI-02), Edmonton, July 28-Aug. 1, 2002, pp. 100-105.
174. G. Brewka, I. Niemelä, and T. Syrjänen Logic programs with ordered disjunction Comput. Intell. 20 2 2004 335 357
175. R. Confalonieri, J.C. Nieves, M. Osorio, and J. Vázquez-Salceda Possibilistic semantics for logic programs with ordered disjunction S. Link, H. Prade, Proc. 6th Inter. Symp. on Foundations of Information and Knowledge Systems (FoIKS 2010) Sofia, February 15-19 Lecture Notes in Computer Science vol. 5956 2010 Springer 133 152
176. S. Benferhat, D. Dubois, S. Kaci, and H. Prade Bridging logical, comparative, and graphical possibilistic representation frameworks S. Benferhat, P. Besnard, Proc. 6th Europ. Conf. on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2001) Toulouse, Sept. 19-21 Lecture Notes in Computer Science vol. 2143 2001 Springer 422 431 (Pubitemid 33334691)
177. S. Benferhat, D. Dubois, S. Kaci, and H. Prade Graphical readings of possibilistic logic bases J.S. Breese, D. Koller, Proc. 17th Conf. in Uncertainty in Artificial Intelligence (UAI 01) Seattle, Aug. 2-5 2001 Morgan Kaufmann 24 31
178. S. Benferhat, and S. Kaci Logical representation and fusion of prioritized information based on guaranteed possibility measures: Application to the distance-based merging of classical bases Artificial Intelligence 148 2003 291 333
179. S. Benferhat, D. Dubois, L. Garcia, and H. Prade On the transformation between possibilistic logic bases and possibilistic causal networks Internat. J. Approx. Reason. 29 2002 135 173 (Pubitemid 34086636)
180. S. Kaci, and H. Prade Mastering the processing of preferences by using symbolic priorities in possibilistic logic M. Ghallab, C.D. Spyropoulos, N. Fakotakis, N.M. Avouris, Proc. 18th Europ. Conf. on Artificial Intelligence (ECAI 2008) Patras, July 21-25 Frontiers in Artificial Intelligence and Applications vol. 178 2008 IOS Press 376 380
181. S. Benferhat, D. Dubois, S. Kaci, and H. Prade Modeling positive and negative information in possibility theory Int. J. Intell. Syst. 23 2008 1094 1118
182. S. Benferhat, D. Dubois, S. Kaci, H. Prade, Bipolar representation and fusion of preferences on the possibilistic logic framework, in: D. Fensel, F. Giunchiglia, D.L. McGuinness, M.-A. Williams (Eds.), Proc. 8th Inter. Conf. on Principles and Knowledge Representation and Reasoning (KR-02), Toulouse, April 22-25, 2002, pp. 421-448.
183. S. Kaci Logical formalisms for representing bipolar preferences Int. J. Intell. Syst. 23 2008 985 997
184. L.W.N. van der Torre, and E. Weydert Parameters for utilitarian desires in a qualitative decision theory Appl. Intell. 14 3 2001 285 301 (Pubitemid 32542991)
185. R. Gérard, S. Kaci, H. Prade, Ranking alternatives on the basis of generic constraints and examples - A possibilistic approach, in: M.M. Veloso (Ed.), Proc. 20th Inter. Joint Conf. on Artificial Intelligence (IJCAI 2007), Hyderabad, January 6-12, 2007, pp. 393-398.
186. J. Lang Conditional desires and utilities: an alternative logical approach to qualitative decision theory W. Wahlster, Proc. 12th Europ. Conf. on Artificial Intelligence Budapest, Aug. 11-16 1996 John Wiley and Sons Chichester 318 322
187. D. Dubois, D.L. Berre, H. Prade, and R. Sabbadin Using possibilistic logic for modeling qualitative decision: ATMS-based algorithms Fund. Inform. 37 1-2 1999 1 30
188. G. Brewka Answer sets and qualitative decision making Synthese 146 2005 171 187 (Pubitemid 41002116)
189. R. Grabos Qualitative model of decision making C. Bussler, D. Fensel, Proc. 11th Inter. Conf. on Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2004) Varna, September 2-4 Lecture Notes in Computer Science vol. 3192 2004 Springer 480 489
190. S. Benferhat, D. Dubois, S. Kaci, and H. Prade Bipolar possibility theory in preference modeling: Representation, fusion and optimal solutions Inform. Fusion 7 1 2006 135 150 (Pubitemid 43133036)
191. R. Felix Aggregation of partly inconsistent preference information E. Hüllermeier, R. Kruse, F. Hoffmann, Proc. 13th Inter. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2010). Part II, Applications Dortmund, June 28-July 2 Communications in Computer and Information Science vol. 81 2010 Springer 178 187
192. J. Lang, and L. van der Torre Preference change triggered by belief change: A principled approach G. Bonanno, B. Löwe, W. van der Hoek, Revised Selected Papers of the 8th International Conference on Logic and the Foundations of Game and Decision Theory (LOFT 8) Amsterdam, July 3-5, 2008 Lecture Notes in Computer Science vol. 6006 2010 Springer 86 111
193. J. van Benthem, and F. Liu Dynamic logic of preference upgrade J. Appl. Non-Classical Logics 17 2 2007 157 182
194. L. Chen, and P. Pu Preference-based organization interfaces: Aiding user critiques in recommender systems C. Conati, K.F. McCoy, G. Paliouras, Proc. 11th Inter. Conf. on User Modeling (UM 2007) Corfu, June 25-29 Lecture Notes in Computer Science vol. 4511 2007 Springer 77 86 (Pubitemid 350270970)
195. U. Rafique, and S.-Y. Huang Preference generation for autonomous agents J. Dix, C. Witteveen, Proc. 8th German Conference on Multiagent System Technologies (MATES10) Leipzig, Sept. 27-29 Lecture Notes in Computer Science vol. 6251 2010 Springer 173 184
196. D. Dubois, H. Fargier, and H. Prade Possibility theory in constraint satisfaction problems: Handling priority, preference and uncertainty Appl. Intell. 6 1996 287 309 (Pubitemid 126709681)
197. M.C. Cooper Reduction operations in fuzzy or valued constraint satisfaction Fuzzy Sets Syst. 134 2003 311 342
198. S. Bistarelli, U. Montanari, F. Rossi, T. Schiex, G. Verfaillie, and H. Fargier Semiring-based CSPs and valued CSPs: Frameworks, properties, and comparison Constraints 4 1999 199 240 (Pubitemid 129579725)
199. H. Fargier, and J. Lang Uncertainty in constraint satisfaction problems: a probabilistic approach M. Clarke, R. Kruse, S. Moral, Proc. 2nd Europ. Conf. Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU93) Granada, Nov. 8-10 Lecture Notes in Computer Science vol. 747 1993 Springer 97 104
200. T. Walsh Stochastic constraint programming F. van Harmelen, Proc. of the 15th Europ. Conf. on Artificial Intelligence (ECAI2002), Lyon 2002 IOS Press 111 115
201. A. Tarim, S. Manandhar, and T. Walsh Stochastic constraint programming: A scenario-based approach Constraints 11 2006 53 80
202. T. Balafoutis, and K. Stergiou Algorithms for stochastic CSPs F. Benhamou, Proc. 12th Inter. Conf. on Principles and Practice of Constraint Programming (CP06) Nantes, Sept. 25-29 Lecture Notes in Computer Science vol. 4204 2006 Springer 44 58 (Pubitemid 44618730)
203. D. Dubois, H. Fargier, and P. Fortemps Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge Europ. J. Oper. Res. 147 2003 231 252
204. D. Dubois, and H. Prade A bipolar possibilistic representation of knowledge and preferences and its applications I. Bloch, A. Petrosino, A. Tettamanzi, Revised Selected Papers of the 6th International Workshop on Fuzzy Logic and Applications (WILF05) Crema, Italy, Sept. 15-17 Lecture Notes in Computer Science vol. 3849 2005 Springer 1 10
205. D. Dubois, and H. Prade An introduction to bipolar representations of information and preference Int. J. Intell. Syst. 23 2008 866 877
206. D. Dubois, and H. Prade An overview of the asymmetric bipolar representation of positive and negative information in possibility theory Fuzzy Sets Syst. 160 2009 1355 1366
207. J. Cacioppo, W. Gardner, and G. Bernston Beyond bipolar conceptualizations and measures: The case of attitudes and evaluative space Pers. Soc. Psychol. Rev. 1 1 1997 3 25
208. R. Da Silva Neves, E. Raufaste, A psychological study of bipolarity in the possibilistic framework, in: 10th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU04), 2004, pp. 975-981.
209. S. Bistarelli, M.S. Pini, F. Rossi, and K.B. Venable Uncertainty in bipolar preference problems C. Bessiere, Proc. 13th Inter. Conf. on Principles and Practice of Constraint Programming (CP 2007) Providence, RI, Sept. 23-27 Lecture Notes in Computer Science vol. 4741 2007 Springer 782 789
210. S. Bistarelli, M. Pini, F. Rossi, and K.B. Venable From soft constraints to bipolar preferences: Modelling framework and solving issues J. Exp. Theor. Artificial Intelligence 22 2010 135 158
211. S. de Givry, J. Larrosa, P. Meseguer, and T. Schiex Solving Max-SAT as weighted CSP F. Rossi, Proc. of 9th Inter. Conf. on Principles and Practice of Constraint Programming (CP03) Kinsale, Ireland, Sept. 29-Oct. 3 Lecture Notes in Computer Science vol. 2833 2003 Springer 363 376
212. M.C. Cooper, and T. Schiex Arc consistency for soft constraints Artificial Intelligence 154 2004 199 227
213. D.A. Cohen, M.C. Cooper, P. Jeavons, and A.A. Krokhin The complexity of soft constraint satisfaction Artificial Intelligence 170 2006 983 1016 (Pubitemid 44037535)
214. C. Domshlak, S.D. Prestwich, F. Rossi, K.B. Venable, and T. Walsh Hard and soft constraints for reasoning about qualitative conditional preferences J. Heuristics 12 2006 263 285 (Pubitemid 43724146)
215. M.C. Cooper, S. de Givry, T. Schiex, Optimal soft arc consistency, in: M.M. Veloso (Ed.), IJCAI 2007, Proc. 20th Inter. Joint Conf. on Artificial Intelligence, Hyderabad, India, Jan. 6-12, 2007, pp. 68-73.
216. M.C. Cooper Minimization of locally defined submodular functions by optimal soft arc consistency Constraints 13 2008 437 458
217. S. Bistarelli, U. Montanari, and F. Rossi Soft constraint logic programming and generalized shortest path problems J. Heuristics 8 2002 25 41 (Pubitemid 34172761)
218. S. Bistarelli, and F. Bonchi Soft constraint based pattern mining Data Knowl. Eng. 62 2007 118 137 (Pubitemid 46603196)
219. S. Bistarelli, and F. Santini A nonmonotonic soft concurrent constraint language for SLA negotiation Electron. Notes Theoret. Comput. Sci. 236 2009 147 162
220. B. Faltings, M. Torrens, and P. Pu Solution generation with qualitative models of preferences Comput. Intell. 20 2 2004 246 263
221. U. Junker Preference-based problem solving for constraint programming F. Fages, F. Rossi, S. Soliman, Recent Advances in Constraints, Revised Selected Papers of 12th Annual ERCIM Inter. Workshop on Constraint Solving and Constraint Logic Programming, CSCLP 2007 Rocquencourt, June 7-8, 2007 Lecture Notes in Computer Science vol. 5129 2008 Springer 109 126
222. A. Gerevini, D. Long, Preferences and soft constraints in PDDL3, in: Proc. ICAPS Workshop on Planning with Preferences and Soft Constraints, Glasgow, 2006.
223. P.H. Tu, T.C. Son, and E. Pontelli CPP: A constraint logic programming based planner with preferences C. Baral, G. Brewka, J.S. Schlipf, LPNMR Lecture Notes in Computer Science vol. 4483 2007 Springer 290 296
224. J.A. Baier, C. Fritz, M. Bienvenu, S.A. McIlraith, Beyond classical planning: Procedural control knowledge and preferences in state-of-the-art planners, in: D. Fox, C.P. Gomes (Eds.), Proc. of the 23rd AAAI Conf. on Artificial Intelligence (AAAI 2008), Chicago, July 13-17, 2008, pp. 1509-1512.
225. S. Sohrabi, J.A. Baier, S.A. McIlraith, HTN planning with preferences, in: C. Boutilier (Ed.), Proc. of the 21st Inter. Joint Conf. on Artificial Intelligence (IJCAI 2009) Pasadena, July 11-17, 2009, pp. 1790-1797.
226. S. Agarwal, S. Lamparter, User preference based automated selection of web service compositions, in: Proc. ICSOC Workshop on Dynamic Web Processes, IBM, 2005, pp. 1-12.
227. S. Lamparter, A. Ankolekar, R. Studer, S. Grimm, Preference-based selection of highly configurable web services, in: C. Williamson, M.E. Zurko, P.F. Patel-Schneider, P.J. Shenoy (Eds.), Proc. of the 16th Inter. Conf. on World Wide Web (WWW 2007), Banff, Alberta, May 8-12, 2007, pp. 1013-1022. (Pubitemid 47582331)
228. C. Schröpfer, M. Binshtok, S.E. Shimony, A. Dayan, R.I. Brafman, P. Offermann, and O. Holschke Introducing preferences over NFPs into service selection in SOA E.D. Nitto, M. Ripeanu, Revised Selected Papers of the Inter. Workshops on Service-Oriented Computing (ICSOC 2007) Vienna, Austria, Sept. 17 Lecture Notes in Computer Science vol. 4907 2007 Springer 68 79
229. N. Lin, U. Kuter, and E. Sirin Web service composition with user preferences Proc. of the 5th European Semantic Web Conference (ESWC) Lecture Notes in Computer Science vol. 5021 2008 Springer 629 643
230. G.R. Santhanam, S. Basu, and V. Honavar TCP-compose? - a TCP-net based algorithm for efficient composition of web services using qualitative preferences Proc. of Inter. Conf. on Service-Oriented Computing Lecture Notes in Computer Science vol. 5364 2008 Springer 453 467
231. S. Agarwal, S. Lamparter, and R. Studer Making Web services tradable: A policy-based approach for specifying preferences on Web service properties J. Web Sem. 7 2009 11 20
232. S. Sohrabi, and S.A. McIlraith Preference-based web service composition: A middle ground between execution and search P.F. Patel-Schneider, Y. Pan, P. Hitzler, P. Mika, L. Zhang, J.Z. Pan, I. Horrocks, B. Glimm, Revised Selected Papers, Part I, of the 9th International Semantic Web Conference (ISWC 2010) Shanghai, November 7-11 Lecture Notes in Computer Science vol. 6496 2010 Springer 713 729
233. J. Thangarajah, J. Harland, N. Yorke-Smith, A soft COP model for goal deliberation in a BDI agent, in: Proc. of 6th Inter. CP07 Workshop on Constraint Modelling and Reformulation, Providence, RI, 2007.
234. K.J. Arrow, A.K. Sen, K. Suzumura, Handbook of Social Choice and Welfare 2002 North-Holland
235. T. Walsh Representing and reasoning with preferences AI Magazine 28 4 2007 59 70
236. J. Lang Logical preference representation and combinatorial vote Ann. Math. Artif. Intell. 42 2004 37 71 (Pubitemid 39046778)
237. V. Conitzer, T. Sandholm, Communication complexity of common voting rules, in: Proc. 6th ACM Conference on Electronic Commerce (EC-2005), Vancouver, June 5-8, 2005, pp. 78-87. (Pubitemid 43048667)
238. V. Conitzer, T. Sandholm, Nonexistence of voting rules that are usually hard to manipulate, in: Proc. 21st National Conf. on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conf., Boston, July 16-20, 2006.
239. V. Conitzer, T. Sandholm, and J. Lang When are elections with few candidates hard to manipulate J. ACM 54 3 2007 art. 14
240. Y. Chevaleyre, P.E. Dunne, U. Endriss, J. Lang, M. Lemaître, N. Maudet, J.A. Padget, S. Phelps, J.A. Rodríguez-Aguilar, and P. Sousa Issues in Multiagent Resource Allocation Informatica (Slovenia) 30 1 2006 3 31 (Pubitemid 43266930)
241. T. Walsh, Uncertainty in preference elicitation and aggregation, in: Proc. 22nd AAAI Conf. on Artificial Intelligence, Vancouver, July 22-26, 2007, pp. 3-8. (Pubitemid 350149545)
242. M.S. Pini, F. Rossi, K.B. Venable, T. Walsh, Incompleteness and incomparability in preference aggregation, in: M.M. Veloso (Ed.), Proc. 20th Inter. Joint Conf. on Artificial Intelligence (IJCAI07), Hyderabad, India, Jan. 6-12, 2007, pp. 1464-1469.
243. K. Konczak, J. Lang, Voting procedures with incomplete preferences, in: Proc. IJCAI-05 Workshop on Advances in Preference Handling, 2005, pp. 124-129.
244. Y. Chevaleyre, U. Endriss, J. Lang, and N. Maudet Preference handling in combinatorial domains: From AI to social choice AI Magazine 29 4 2008 37 46
245. P. Cramton, Y. Shoham, R. Steinberg, Combinatorial Auctions 2006 MIT Press
246. C. Boutilier, H.H. Hoos, Bidding languages for combinatorial auctions, in: B. Nebel (Ed.), Proc. 7th Inter. Joint Conf. on Artificial Intelligence (IJCAI 2001), Seattle, Morgan Kaufmann, Aug. 4-10, 2001, pp. 1211-1217.
247. T. Sandholm Algorithm for optimal winner determination in combinatorial auctions Artificial Intelligence 135 2002 1 54 (Pubitemid 34110153)
248. N. Nisan Bidding languages for combinatorial auctions P. Cramton, Y. Shoham, R. Steinberg, Combinatorial Auctions 2006 MIT Press
249. S. Bouveret, and J. Lang Efficiency and envy-freeness in fair division of indivisible goods: Logical representation and complexity J. Artif. Intell. Res. 32 2008 525 564
250. U. Endriss, N. Maudet, F. Sadri, and F. Toni Negotiating socially optimal allocations of resources J. Artif. Intell. Res. 25 2006 315 348 (Pubitemid 43800049)
251. X. Luo, N.R. Jennings, and N. Shadbolt Acquiring user tradeoff strategies and preferences for negotiating agents: A default-then-adjust method Int. J. Man-Mach. Stud. 64 2006 304 321 (Pubitemid 43267743)
252. S.S. Fatima, M.I. Wooldridge, N.R. Jennings, An analysis of feasible solutions for multi-issue negotiation involving nonlinear utility functions, in: C. Sierra, C. Castelfranchi, K.S. Decker, J.S. Sichman (Eds.), Proc. 8th Inter. Joint Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2009), vol. 2, Budapest, May 10-15, 2009, pp. 1041-1048.
253. Y. Chevaleyre, U. Endriss, and N. Maudet Simple negotiation schemes for agents with simple preferences: sufficiency, necessity and maximality Autonomous Agents and Multi-Agent Systems 20 2010 234 259
254. U. Chajewska, L. Getoor, J. Norman, Y. Shahar, Utility elicitation as a classification problem, in: Proc. UAI-98, Uncertainty in Artificial Intelligence, 1998, pp. 79-88.
255. P. Haddawy, V. Ha, A. Restificar, B. Geisler, and J. Miyamoto Preference elicitation via theory refinement J. Mach. Learn. Res. 4 2003 317 337
256. P. Viappiani, P. Pu, and B. Faltings Preference-based search with adaptive recommendations AI Commun. 21 2-3 2008 155 175
257. M. Pazzani A framework for collaborative, content-based and demographic filtering Artificial Intelligence Rev. 13 5-6 1999 393 408 (Pubitemid 30594816)
258. P. Perny, J. Zucker, Preference-based search and machine learning for collaborative filtering: the "film-conseil" movie recommender system, Information Interaction Intelligence 1 (1).
259. T. Joachims, Optimizing search engines using clickthrough data, in: KDD-2002, 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, 2002.
260. F. Radlinski, T. Joachims, Learning to rank from implicit feedback, in: Proc. KDD-05, 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA, 2005.
261. J. Fürnkranz, E. Hüllermeier, Preference Learning 2010 Springer Berlin, Heidelberg
262. W. Cohen, R. Schapire, and Y. Singer Learning to order things J. Artif. Intell. Res. 10 1 1999 243 270 (Pubitemid 30573909)
263. T. Kamishima, H. Kazawa, and S. Akaho A survey and empirical comparison of object ranking methods J. Fürnkranz, E. Hüllermeier, Preference Learning 2010 Springer Berlin, Heidelberg 181 202
264. K. Brinker, E. Hüllermeier, Case-based label ranking, in: Proc. ECML-06, 17th European Conference on Machine Learning, Springer, Berlin, 2006, pp. 566-573. (Pubitemid 44618863)
265. S. Har-Peled, D. Roth, D. Zimak, Constraint classification: a new approach to multiclass classification, in: Proc. 13th Int. Conf. on Algorithmic Learning Theory, Springer, Lübeck, Germany, 2002, pp. 365-379.
266. W. Cheng, J. Hühn, E. Hüllermeier, Decision tree and instance-based learning for label ranking, in: Proc. 26th Inter. Conf. on Machine Learning (ICML09), Montreal, 2009.
267. S. Vembu, and T. Gärtner Label ranking: A survey J. Fürnkranz, E. Hüllermeier, Preference Learning 2010 Springer
268. J. Fürnkranz, E. Hüllermeier, S. Vanderlooy, Binary decomposition methods for multipartite ranking, in: Proc. ECML/PKDD-2009, European Conference on Machine Learning and Knowledge Discovery in Databases, Bled, Slovenia, 2009.
269. J. Quevedo, E. Montanes, O. Luaces, and J. del Coz Adapting decision DAGs for multipartite ranking Machine Learning and Knowledge Discovery in Databases Lecture Notes in Computer Science vol. 6323 2010 Springer Berlin, Heidelberg
270. J. Fürnkranz, and E. Hüllermeier Pairwise preference learning and ranking Proc. ECML-03, 13th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia 2003 Springer
271. E. Hüllermeier, J. Fürnkranz, W. Cheng, and K. Brinker Label ranking by learning pairwise preferences Artificial Intelligence 172 2008 1897 1917
272. D. Coppersmith, L. Fleischer, A. Rudra, Ordering by weighted number of wins gives a good ranking for weighted tournaments, in: ACM-SIAM Symposium on Discrete Algorithms (SODA), 2006, pp. 776-782. (Pubitemid 43275300)
273. E. Hüllermeier, and J. Fürnkranz On loss functions in label ranking and risk minimization by pairwise learning J. Comput. System Sci. 76 1 2010 49 62
274. P. Flach, and T. Matsubari A simple lexicographic ranker and probability estimator Proc. ECML-07, 17th European Conference on Machine Learning, Warsaw, Poland 2007 Springer
275. F. Yaman, T. Walsh, M. Littman, M. des Jardins, Democratic approximation of lexicographic preference models, in: Proc. ICML-08, Twenty-Fifth International Conference on Machine Learning, Helsinki, Finland, 2008.
276. R. Booth, Y. Chevaleyre, J. Lang, J. Mengin, and C. Sombattheera Learning conditionally lexicographic preference relations H. Coelho, R. Studer, M. Wooldridge, Proc. 19th Europ. Conf. on Artificial Intelligence (ECAI 2010) Lisbon, Aug. 16-20 2010 IOS Press 269 274
277. Y. Chevaleyre, F. Koriche, J. Lang, J. Mengin, and B. Zanuttini Learning ordinal preferences on multiattribute domains: The case of CP-nets J. Furnkranz, E. Hüllermeier, Preference Learning 2010 Springer 273 296
278. W. Cheng, K. Dembczynski, E. Hüllermeier, Label ranking based on the Placket-Luce model, in: J. Fürnkranz, T. Joachims (Eds.), Proc. ICML-2010, International Conference on Machine Learning, Haifa, Israel, 2010.