Qualitative choice logic

Artificial Intelligence

Volumn 157, Issue 1-2, 2004, Pages 203-237

  Brewka, Gerhard ^a   Benferhat, Salem ^b   Le Berre, Daniel ^b  

^a UNIVERSITY OF LEIPZIG   (Germany)

^b UNIVERSITÉ D'ARTOIS   (France)

Author keywords

Nonmonotonic reasoning; Preference handling; Qualitative decision making

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION MAKING; HOTELS; MATHEMATICAL MODELS; MATHEMATICAL OPERATORS; MODAL ANALYSIS; PROBLEM SOLVING; TRANSPORTATION;

LOGICAL LANGUAGES; NONMONOTONIC REASONING; PREFERENCE HANDLING; QUALITATIVE DECISION MAKING;

LOGIC PROGRAMMING;

EID: 2942656990     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artint.2004.04.006     Document Type: Article

References (37)

1. Benferhat S., Cayrol C., Dubois D., Lang J., Prade H. Inconsistency management and prioritized syntax-based entailment. Proc. IJCAI-93, Chambéry, France. 1993;640-645.
2. Benferhat S., Dubois D., Kaci S., Prade H. Bridging logical, comparative and graphical possibilistic representation frameworks. Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty. (ECSQARU'01):2001;422-431 Toulouse, France.
3. Benferhat S., Kaci S. A logical representation and fusion of prioritized information based on guaranteed possibility measures: application to the encoding of distance-based merging for classical bases. Artificial Intelligence. 148:(1-2):2003;291-333.
4. Benferhat S., Dubois D., Prade H. Some syntactic approaches to the handling of inconsistent knowledge bases: a comparative study. Part II: The prioritized case. Orlowska E. Logic at Work. vol. 24:1998;473-511 Physica-Verlag, Heidelberg.
5. Benferhat S., Kaci S., Le Berre D., Williams M.-A. Weakening conflicting information for iterated revision and knowledge integration. Proc. IJCAI-01, Seattle, WA. 2001;109-115.
6. Bistarelli S., Montanari U., Rossi F. Semiring-based constraint solving and optimization. J. ACM. 44:(2):1997;201-236.
7. Boutilier C. Towards a logic for qualitative decision theory. Proc. Principles of Knowledge Representation and Reasoning (KR-94), Bonn. 1994;75-86.
8. Boutilier R.I., Brafman C., Hoos H.H., Poole D. Reasoning with conditional ceteris paribus preference statements. Proc. UAI-99, Stockholm, Sweden. 1999;71-80.
9. Brewka G. Well-founded semantics for extended logic programs with dynamic preferences. J. Artificial Intelligence Res. 4:1996;19-36.
10. Brewka G. Representing meta-knowledge in Poole-systems. Studia Logica. 67:2001;153-165.
11. Brewka G. Logic programming with ordered disjunction. Proc. 17th National Conference on Artificial Intelligence (AAAI-02), Edmonton, AB. 2002;100-105.
12. Brewka G., Eiter T. Preferred answer sets for extended logic programs. Artificial Intelligence. 109:1999;297-356.
13. Brewka G., Niemelä I., Syrjänen T. Implementing ordered disjunction using answer set solvers for normal programs. Proc. JELIA, Cosenza, Italy, 2002. 2002;444-455 Springer, Berlin.
14. S. Coste-Marquis, P. Marquis, On stratified belief base compilations, Ann. Math. Artificial Intelligence, in press.
15. Delgrande J., Schaub T., Tompits H. Logic programs with compiled preferences. Proc. European Conference on Artificial Intelligence, Berlin. 2000;392-398.
16. Dubois D., Hajek P., Prade H. Knowledge-driven versus data-driven logics. J. Logic Language Inform. 9:2000;65-89.
17. Doyle J., Thomason R.H. Background to qualitative decision theory. AI Magazine. 20:(2):1999;55-68.
18. Freuder E.C., Wallace R.J. Partial constraint satisfaction. Artificial Intelligence. 58:(1):1992;21-70.
19. Fargier H., Lang J., Schiex T. Selecting preferred solutions in fuzzy constraint satisfaction problems. Proc. First European Congress on Fuzzy and Intelligent Technologies. 1993.
20. Grosof B. Diplomat: compiling prioritized rules into ordinary logic programs for e-commerce applications. Proc. National Conference on Artificial Intelligence (AAAI-99), Orlando, FL. 1999;912-913. (intelligent systems demonstration abstract).
21. Kraus S., Lehmann D., Magidor M. Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence. 44:1990;167-207.
22. Lang J. Conditional desires and utilities - an alternative logical approach to qualitative decision theory. Proc. 12th European Conference on Artificial Intelligence (ECAI-96), Budapest, Hungary. 1996;318-322.
23. Lang J., van der Torre L., Weydert E. Utilitarian desires. Autonomous Agents and Multi-Agent Systems. 5:(3):2002;329-363.
24. Lehmann D. Another perspective on default reasoning. Ann. Math. Artificial Intelligence. 15:(1):1995;61-82.
25. Lehmann D., Magidor M. What does a conditional knowledge base entail? Artificial Intelligence. 55:1992;1-60.
26. Lifschitz V. Circumscription. Handbook of Logic in AI and Logic Programming. vol. 3:1994;298-352 Oxford University Press, Oxford.
27. McCarthy J. Circumscription - A form of nonmonotonic reasoning. Artificial Intelligence. 13:1980;27-39.
28. McCarthy J. Applications of circumscription to formalizing common sense knowledge. Artificial Intelligence. 28:1986;89-116.
29. Moinard Y. Note about cardinality-based circumscription. Artificial Intelligence. 119:(1-2):2000;259-273.
30. Moore R.C. Semantical considerations on nonmonotonic logic. Artificial Intelligence. 25:1985;75-94.
31. Poole D. The independent choice logic for modelling multiple agents under uncertainty. Artificial Intelligence. 94:(1-2):1997;7-56.
32. Reiter R. A logic for default reasoning. Artificial Intelligence. 13:1980;81-132.
33. Schaub T., Wang K. A comparative study of logic programs with preference. Proc. IJCAI-01, Seattle, WA. 2001;631-637.
34. Schiex T., Fargier H., Verfaillie G. Valued constraint satisfaction problems: Hard and easy problems. Proc. IJCAI-95, Montreal, Quebec. 1995;631-637.
35. Soininen T., Niemelä I., Tiihonen J., Sulonen R. Representing configuration knowledge with weight constraint rules. Proc. of the AAAI Spring Symposium on Answer Set Programming: Towards Efficient and Scalable Knowledge, Stanford, CA. 2001.
36. Thomason R. Desires and defaults: a framework for planning with inferred goals. Proc. 7th Conference on Principles of Knowledge Representation and Reasoning (KR-2000), Breckenridge, CO. 2000;702-713.
37. van der Torre L., Weydert E. Parameters for utilitarian desires in a qualitative decision theory. Appl. Intelligence. 14:2001;285-301.