References

  1. S. Abiteboul, R. Hull & V. Vianu (1995): Foundations of Databases. Addison-Wesley.
  2. O. Arieli, M. Denecker & M. Bruynooghe (2007): Distance semantics for database repair. Annals of Mathematics and Artificial Intelligence 50(3), pp. 389–415, doi:10.1007/s10472-007-9074-1.
  3. K. J. Arrow (1963): Social Choice and Individual Values, 2nd edition. John Wiley and Sons.
  4. C. Baral, S. Kraus & J. Minker (1991): Combining Multiple Knowledge Bases. IEEE Transactions on Knowledge and Data Engineering 3(2), pp. 208–220, doi:10.1109/69.88001.
  5. C. Baral, S. Kraus, J. Minker & V. S. Subrahmanian (1992): Combining knowledge bases consisting of first-order theories. Computational Intelligence 8(1), doi:10.1016/0004-3702(80)90014-4.
  6. F. Belardinelli & U. Grandi (2019): A Social Choice Theoretic Perspective on Database Aggregation (Extended Abstract). In: Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
  7. H. A. Blair & V. S. Subrahmanian (1989): Paraconsistent Logic Programming. Theoretical Computer Science 68(2), pp. 135–154, doi:10.1016/0304-3975(89)90126-6.
  8. R. Booth, E. Awad & I. Rahwan (2014): Interval Methods for Judgment Aggregation in Argumentation. In: Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR).
  9. M. Caminada & G. Pigozzi (2011): On judgment aggregation in abstract argumentation. Autonomous Agents and Multiagent Systems 22(1), pp. 64–102, doi:10.1007/s10458-009-9116-7.
  10. Ashok K. Chandra & Philip M. Merlin (1977): Optimal Implementation of Conjunctive Queries in Relational Data Bases. In: Proceedings of the Ninth Annual ACM Symposium on Theory of Computing, STOC '77. ACM, New York, NY, USA, pp. 77–90, doi:10.1145/800105.803397.
  11. W. Chen & U. Endriss (2017): Preservation of Semantic Properties during the Aggregation of Abstract Argumentation Frameworks. In: Proceedings of the 16th Conference on Theoretical Aspects of Rationality and Knowledge (TARK), doi:10.1007/978-3-540-77684-0_4.
  12. F. Dietrich & C. List (2007): Arrow's Theorem in Judgment Aggregation. Social Choice and Welfare 29(1), pp. 19–33, doi:10.1007/s00355-006-0196-x.
  13. F. Dietrich & C. List (2007): Judgment Aggregation by Quota Rules: Majority Voting Generalized. Journal of Theoretical Politics 19(4), pp. 391–424, doi:10.1016/0022-0531(75)90062-9.
  14. E. Dokow & R. Holzman (2010): Aggregation of binary evaluations. Journal of Economic Theory 145(2), pp. 495–511, doi:10.1016/j.jet.2007.10.004.
  15. J. Doyle & M. P. Wellman (1991): Impediments to Universal Preference-based Default Theories. Artificial Intelligence 49(1), pp. 97–128, doi:10.1016/0004-3702(91)90007-7.
  16. U. Endriss (2016): Judgment Aggregation. In: F. Brandt, V. Conitzer, U. Endriss, J. Lang & A. D. Procaccia: Handbook of Computational Social Choice. Cambridge University Press, doi:10.1017/CBO9781107446984.018.
  17. U. Endriss & U. Grandi (2014): Binary Aggregation by Selection of the Most Representative Voter. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI-2014).
  18. U. Endriss & U. Grandi (2017): Graph Aggregation. Artificial Intelligence 245, pp. 86–114, doi:10.1016/j.artint.2017.01.001.
  19. P. Everaere, S. Konieczny & P. Marquis (2015): Belief Merging versus Judgment Aggregation. In: Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
  20. A. Gionis, H. Mannila & P. Tsaparas (2007): Clustering aggregation. ACM Transactions on Knowledge Discovery from Data 1(1), pp. 4, doi:10.1145/1217299.1217303.
  21. U. Grandi & U. Endriss (2011): Binary Aggregation with Integrity Constraints. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI).
  22. U. Grandi & U. Endriss (2013): Lifting Integrity Constraints in Binary Aggregation. Artificial Intelligence 199–200, pp. 45–66, doi:10.1016/j.artint.2013.05.001.
  23. E. Gregoire & S. Konieczny (2006): Logic-based approaches to information fusion. Information Fusion 7(1), pp. 4 – 18, doi:10.1016/j.inffus.2005.08.001. Logic-based Approaches to Information Fusion.
  24. F. van Harmelen, V. Lifschitz & B. Porter (2007): Handbook of Knowledge Representation. Elsevier Science, San Diego, USA.
  25. J. Hintikka (1962): Knowledge and Belief: An Introduction to the Logic of the Two Notions. Cornell University Press.
  26. B. Kimelfeld, P. Kolaitis & J. Stoyanovich (2018): Computational Social Choice Meets Databases. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), doi:10.24963/ijcai.2018/44.
  27. S. Konieczny (2000): On the Difference between Merging Knowledge Bases and Combining them. In: Proceedings of the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR).
  28. S Konieczny, J Lang & P Marquis (2004): DA2 merging operators. Artificial Intelligence 157(1), pp. 49 – 79.
  29. S. Konieczny & R. Pino Pérez (2002): Merging Information Under Constraints: A Logical Framework. Journal of Logic and Computation 12(5), pp. 773–808, doi:10.1093/logcom/12.5.773.
  30. Sébastien Konieczny, Jérôme Lang & Pierre Marquis (2002): Distance Based Merging: A General Framework and some Complexity Results. In: Proceedings of the Eights International Conference on Principles and Knowledge Representation and Reasoning (KR).
  31. P. Liberatore & M. Schaerf (1998): Arbitration (or How to Merge Knowledge Bases). IEEE Transactions on Knowledge and Data Engineering 10(1), pp. 76–90, doi:10.1109/69.667090.
  32. L. Libkin (2015): How to Define Certain Answers. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI).
  33. Jinxin Lin & Alberto O. Mendelzon (1996): Merging Databases under Constraints. International Journal of Cooperative Information Systems 7, pp. 55–76, doi:10.1142/S021821579200009X.
  34. D. Maier, J. Ullman & M. Vardi (1984): On the Foundations of the Universal Relation Model. ACM Transactions on Database Systems 9(2), pp. 283–308, doi:10.1145/329.318580.
  35. P. Maynard-Zhang & D. J. Lehmann (2003): Representing and Aggregating Conflicting Beliefs. Journal of Artificial Intelligence Research 19, pp. 155–203, doi:10.1613/jair.1206.
  36. M. Miller & D. Osherson (2009): Methods for distance-based judgment aggregation. Social Choice and Welfare 32(4), pp. 575–601, doi:10.1007/s00355-008-0340-x.
  37. D. Porello & U. Endriss (2012): Ontology merging as social choice: Judgment aggregation under the open world assumption. Journal of Logic and Computation 24, pp. 1229–1249, doi:10.1093/logcom/exs056.
  38. V. S. Subrahmanian (1992): Paraconsistent Disjunctive Deductive Databases. Theoretical Computer Science 93(1), pp. 115–141, doi:10.1016/0304-3975(92)90214-Z.
Comments and questions to: eptcs@eptcs.org
For website issues: webmaster@eptcs.org