Note about cardinality-based circumscription

Artificial Intelligence

Volumn 119, Issue 1, 2000, Pages 259-273

  Moinard, Yves ^a  

^a CAMPUS DE BEAULIEU   (France)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; COMPUTER SIMULATION; KNOWLEDGE REPRESENTATION; LOGIC PROGRAMMING; THEOREM PROVING;

BELIEF REVISION THEORY; CIRCUMSCRIPTION;

ARTIFICIAL INTELLIGENCE;

EID: 0033703181     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0004-3702(00)00018-7     Document Type: Article

References (19)

1. Bossu G., Siegel P. Saturation, nonmonotonic reasoning and the closed-world assumption. Artificial Intelligence. Vol. 25:(1):1985;13-63.
2. Cadoli M., Eiter T., Gottlob G. An efficient method for eliminating varying predicates from a circumscription. Artificial Intelligence. Vol. 54:(3):1992;397-410.
3. Costello T. The expressive power of circumscription. Artificial Intelligence. Vol. 104:(1-2):1998;313-329.
4. de Kleer J., Konolige K. Eliminating the fixed predicates from a circumscription. Artificial Intelligence. Vol. 39:(3):1989;391-398.
5. Heuerding A. LWBtheory: Information about some Propositional Logics via the WWW. Bull. IGPL. Vol. 5:(1):1997;169-174.
6. Kraus S., Lehmann D., Magidor M. Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence. Vol. 44:(1-2):1990;167-207.
7. Lehmann D., Magidor M. What does a conditional knowledge base entail? Artificial Intelligence. Vol. 55:(1):1992;1-60.
8. Liberatore P., Schaerf M. Relating belief revision and circumscription. Proc. IJCAI-95, Montreal, Quebec. 1995;1557-1563 Morgan Kaufmann, San Mateo, CA.
9. Liberatore P., Schaerf M. Reducing belief revision to circumscription (and vice versa). Artificial Intelligence. Vol. 93:(1-2):1997;261-296.
10. Lifschitz V. Computing circumscription. Proc. IJCAI-85, Los Angeles, CA. 1985;121-127 Morgan Kaufmann, San Mateo, CA.
11. Makinson D. General patterns in nonmonotonic reasoning. Gabbay D.M., Hogger C., Robinson J. Handbook of Logic in Artificial Intelligence and Logic Programming, Vol. 3: Non-Monotonic and Uncertainty Reasoning. 1994;35-110 Oxford University Press, Oxford.
12. McCarthy J. Circumscription - A form of non-monotonic reasoning. Artificial Intelligence. Vol. 13:(1-2):1980;27-39.
13. McCarthy J. Application of circumscription to formalizing common sense knowledge. Artificial Intelligence. Vol. 28:(1):1986;89-116.
14. Moinard Y., Rolland R. Propositional circumscriptions, Technical Report, INRIA-IRISA, PI 1211, Rennes, France. 1998;. http://www.irisa.fr/EXTERNE/bibli/pi/1211/1211.html.
15. Moinard Y., Rolland R. Preferential entailments, extensions and reductions of the vocabulary, Technical Report, INRIA-IRISA, PI 1273, Rennes, France. 1999;. http://www.irisa.fr/EXTERNE/bibli/pi/1273/1273.html.
16. Perlis D., Minker J. Completeness results for circumscription. Artificial Intelligence. Vol. 28:(1):1986;29-42.
17. Satoh K. A probabilistic interpretation for lazy nonmonotonic reasoning. Proc. AAAI-90, Boston, MA. 1990;659-664 MIT Press, Cambridge, MA.
18. Sharma N., Colomb R. Towards an integrated characterisation of model-based diagnosis and configuration through circumscription policies, Technical Report 364. 1996;Department of Computer Science, The University of Queensland, Brisbane, Queensland, Australia.
19. Shoham Y. Reasoning about Change. 1988;MIT Press, Cambridge, MA.