DA2 merging operators

Artificial Intelligence

Volumn 157, Issue 1-2, 2004, Pages 49-79

  Konieczny, S ^a   Lang, J ^a   Marquis, P ^b  

^a IRIT   (France)

^b UNIVERSITÉ D'ARTOIS   (France)

Author keywords

Belief merging; Computational complexity; Knowledge representation

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL COMPLEXITY; CONSTRAINT THEORY; ENCODING (SYMBOLS); FUNCTIONS; KNOWLEDGE REPRESENTATION; MATHEMATICAL MODELS; POLYNOMIALS; QUERY LANGUAGES; SET THEORY;

BELIEF MERGING; INTEGRATING CONSTRAINTS; MERGING OPERATORS; MERGING PROCESS;

MATHEMATICAL OPERATORS;

EID: 2942665708     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artint.2004.04.008     Document Type: Article

References (39)

1. Bloch I., Hunter A. Fusion: General concepts and characteristics. Internat. J. Intelligent Syst. 16:(10):2001;1107-1134. (special issue on Data and Knowledge Fusion).
2. Revesz P.Z. On the semantics of arbitration. Internat. J. Algebra Comput. 7:(2):1997;133-160.
3. Lin J., Mendelzon A.O. Knowledge base merging by majority. Dynamic Worlds: From the Frame Problem to Knowledge Management. 1999;Kluwer Academic, Dordrecht.
4. Liberatore P., Schaerf M. Arbitration (or how to merge knowledge bases). IEEE Trans. Knowledge Data Engrg. 10:(1):1998;76-90.
5. Konieczny S., Pino Pérez R. Merging with integrity constraints. Proceedings of the Fifth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU'99), London. Lecture Notes in Artificial Intelligence. vol. 1638:1999;233-244 Springer, Berlin.
6. Benferhat S., Dubois D., Kaci S., Prade H. Encoding information fusion in possibilistic logic: a general framework for rational syntactic merging. Proceedings of the Fourteenth European Conference on Artificial Intelligence (ECAI'00), Berlin. 2000;3-7.
7. Baral C., Kraus S., Minker J. Combining multiple knowledge bases. IEEE Trans. Knowledge Data Engrg. 3:(2):1991;208-220.
8. Baral C., Kraus S., Minker J., Subrahmanian V.S. Combining knowledge bases consisting of first-order theories. Comput. Intelligence. 8:(1):1992;45-71.
9. Konieczny S. On the difference between merging knowledge bases and combining them. Proceedings of the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR'00), Breckenridge, CO. 2000;135-144.
10. Nebel B. A knowledge level analysis of belief revision. Proceedings of the First International Conference on the Principles of Knowledge Representation and Reasoning (KR'89), Toronto. 1989;301-311.
11. Hansson S.O. Revision of belief sets and belief bases. Handbook of Defeasible Reasoning and Uncertainty Management Systems. vol. 3:1998;17-75.
12. Konieczny S., Pino Pérez R. On the frontier between arbitration and majority. Proceedings of the Eighth International Conference on Principles of Knowledge Representation and Reasoning (KR'02), Toulouse. 2002;109-118.
13. Konieczny S., Pino Pérez R. Merging information under constraints: A qualitative framework. J. Logic Comput. 12:(5):2002;773-808.
14. Papadimitriou C.H. Computational Complexity. 1994;Addison-Wesley, Reading, MA.
15. Eiter T., Gottlob G. On the complexity of propositional knowledge base revision, updates, and counterfactuals. Artificial Intelligence. 57:(2-3):1992;227-270.
16. Wagner K.W. More complicated questions about maxima and minima, and some closures of NP. Theoretical Comput. Sci. 51:1987;53-80.
17. Revesz P.Z. On the semantics of theory change: arbitration between old and new information. Proceedings of the Twelfth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Databases (PODS'93), Washington, DC. 1993;71-92.
18. Konieczny S., Pino Pérez R. On the logic of merging. Proceedings of the Sixth International Conference on Principles of Knowledge Representation and Reasoning (KR'98), Trento, Italy. 1998;488-498.
19. Dalal M. Investigations into a theory of knowledge base revision: preliminary report. Proceedings of the Seventh American National Conference on Artificial Intelligence (AAAI'88), St. Paul, MN. 1988;475-479.
20. Rescher N., Manor R. On inference from inconsistent premises. Theory and Decision. 1:1970;179-219.
21. Brewka G. Preferred subtheories: an extended logical framework for default reasoning. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI'89), Detroit, MI. 1989;1043-1048.
22. Benferhat S., Cayrol C., Dubois D., Lang J., Prade H. Inconsistency management and prioritized syntax-based entailment. Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI'93), Chambéry, France. 1993;640-645.
23. Benferhat S., Dubois D., Prade H. Some syntactic approaches to the handling of inconsistent knowledge bases: A comparative study, Part 1: The flat case. Studia Logica. 58:1997;17-45.
24. Benferhat S., Dubois D., Lang J., Prade H., Saffioti A., Smets P. A general approach for inconsistency handling and merging information in prioritized knowledge bases. Proceedings of the Sixth International Conference on Principles of Knowledge Representation and Reasoning (KR'98), Trento, Italy. 1998;466-477.
25. Fagin R., Ullman J., Vardi M. On the semantics of updates in databases. Proceedings of the Second ACM SIGACT SIGMOD Symposium on Principles of Database Systems (PODS'83), Atlanta, GA. 1983;352-365.
26. Nebel B. Belief revision and default reasoning: Syntax-based approaches. Proceedings of the Second International Conference on the Principles of Knowledge Representation and Reasoning (KR'91), Cambridge, MA. 1991;417-428.
27. Ginsberg M. Counterfactuals. Artificial Intelligence. 30:1986;35-79.
28. Alchourrón C.E., Gärdenfors P., Makinson D. On the logic of theory change: Partial meet contraction and revision functions. J. Symbolic Logic. 50:1985;510-530.
29. Lafage C., Lang J. Logical representation of preferences for group decision theory. Proceedings of the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR'00), Breckenridge, CO. 2000;457-468.
30. Yager R.R. On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans. Systems Man Cybernetics. 18:1998;183-190.
31. J.L. Marichal, Aggregation operators for multicriteria decision aid, Ph.D. Thesis, Université de Liege, 1999.
32. Murofushi T., Sugeno M. An interpretation of fuzzy measures and the choquet integral as an integral with respect to a fuzzy measure. Fuzzy Sets Systems. 29:1989;201-227.
33. Pinkas G. Reasoning, nonmonotonicity and learning in connectionist networks that capture propositional knowledge. Artificial Intelligence. 77:1995;203-247.
34. Grégoire E., Sofiane A. Fusing syntax and semantics in knowledge fusion. Proceedings of the Eusflat Conference (Eusflat'2001), Leicester. 2001;414-417.
35. Fargier H., Sabbadin R. Qualitative decision under uncertainty: Back to expected utility. Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI'03), Acapulco, Mexico. 2003;303-308.
36. Nebel B. How hard is it to revise a belief base? Handbook of Defeasible Reasoning and Uncertainty Management Systems, vol. 3: Belief Change. 1998;77-145.
37. Bylander T. The computational complexity of propositional STRIPS planning. Artificial Intelligence. 69:(1-2):1994;165-204.
38. Liberatore P., Schaerf M. Brels: A system for the integration of knowledge bases. Proceedings of the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR'00), Breckenridge, CO. 2000;145-152.
39. Lafage C., Lang J. Propositional distances and preference representation. Proceedings of the Sixth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU'01), Toulouse. 2001;48-59.